diff --git a/util/convert_weights.py b/util/convert_weights.py index c9856d7..d9cdc15 100644 --- a/util/convert_weights.py +++ b/util/convert_weights.py @@ -13,23 +13,74 @@ # 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 +import argparse +import os + +# WIP - DO NOT MERGE +# Requires torch 2.2 and gemma package from https://github.com/google/gemma_pytorch + +## parameters +## model, tokenizer, model type, + +def check_file_exists(value): + if not os.path.exists(str(value)): + raise argparse.ArgumentTypeError("The file %s does not appear to exist." % value) + return value + + +def check_model_types(value): + if str(value).lower() not in ["2b", "7b"]: + raise argparse.ArgumentTypeError("Model type value %s is not in [2b, 7b]." % value) + return value + + +parser = argparse.ArgumentParser() +parser.add_argument( + "--tokenizer", + dest="tokenizer", + default="models/tokenizer.spm", + help="Location of tokenizer file (.model or .spm)", + type=check_file_exists, +) + +parser.add_argument( + "--weights", + dest="weights", + default="models/gemma-2b-it.ckpt", + help="Location of input checkpoint file (.ckpt)", + type=check_file_exists, +) + +parser.add_argument( + "--output_file", + dest="output_file", + default="2bit-f32.sbs", + help="Location to write converted weights", + type=str, +) + +parser.add_argument( + "--model_type", + dest="model_type", + default="2b", + help="Model size / type (2b, 7b)", + type=check_model_types, +) + +args = parser.parse_args() + 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, :, :] @@ -39,23 +90,33 @@ def expand_qkv(qkv_proj: np.array) -> np.array: qkv = np.transpose(qkv, axes=[1,0,2,3]) return qkv -TRANSFORMATIONS = defaultdict( +TRANSFORMATIONS = { + "2b":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? + "self_attn.o_proj.weight": lambda x: x.reshape(2048, 8, 256).transpose([1,0,2]), "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, - }, -) + } + ), + "7b":defaultdict( + lambda: lambda x: x, + { + "embedder.weight": lambda x: np.concatenate([x, np.zeros([128, 3072])], 0), + "self_attn.qkv_proj.weight": lambda x: x.reshape((16, 3, 256, 3072)), + "self_attn.o_proj.weight": lambda x: x.reshape(3072, 16, 256).transpose([1,0,2]), + "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 = { + "2b": { "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), @@ -65,10 +126,22 @@ VALIDATIONS = { "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,), + }, + "7b": { + "embedder.weight": lambda x: x.shape == (256128, 3072), + "model.norm.weight": lambda x: x.shape == (3072,), + "self_attn.qkv_proj.weight": lambda x: x.shape == (16, 3, 256, 3072), + "self_attn.o_proj.weight": lambda x: x.shape == (16, 3072, 256), + "mlp.gate_proj.weight": lambda x: x.shape == (1, 24576, 3072), + "mlp.up_proj.weight": lambda x: x.shape == (1, 24576, 3072), + "mlp.down_proj.weight": lambda x: x.shape == (3072, 24576), + "input_layernorm.weight": lambda x: x.shape == (3072,), + "post_attention_layernorm.weight": lambda x: x.shape == (3072,), + }, } -def param_names(): +def param_names(num_hidden_layers: int): """Return parameter names in the order they are expected for deserialization.""" # note *weight_scaler params are ignored in the forward computation unless @@ -79,62 +152,50 @@ def param_names(): # 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) + ("embedder.weight", ) * 2, # embedder_input_embedding + ("model.norm.weight", ) * 2 # final_norm_scale ] 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) + "self_attn.o_proj.weight", # attn_vec_einsum_w + "self_attn.qkv_proj.weight", # qkv_einsum_w + "mlp.gate_proj.weight", # gating_einsum_w "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) + "mlp.down_proj.weight", # linear_w + "input_layernorm.weight", # pre_attention_norm_scale + "post_attention_layernorm.weight", # pre_ffw_norm_scale ] # fmt: on - for layer in range(18): + for layer in range(num_hidden_layers): 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_type = args.model_type + output_file = args.output_file + + model_config = config.get_model_config(model_type) model_config.dtype = "float32" - - ## this turns on int8 quantization - # model_config.quant = "store_true" - model_config.tokenizer = "models/tokenizer.spm" + model_config.tokenizer = args.tokenizer 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() + model.load_weights(args.weights) + model_dict = dict(model.named_parameters()) + param_order = param_names(model_config.num_hidden_layers) 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" + arr = TRANSFORMATIONS[model_type][layer_name](arr) + check = "OK" if VALIDATIONS[model_type][layer_name](arr) else "FAILED" if check == "FAILED": all_ok = False - - print(f" {name : <60}{str(arr.shape) : <20}{check}") + print(f" {name : <60}{str(arr.shape) : <20}{check}") if all_ok: print("Writing parameters ...") @@ -142,19 +203,10 @@ def convert_weights(): 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" + arr = TRANSFORMATIONS[model_type][layer_name](arr) + check = "OK" if VALIDATIONS[model_type][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) + arr.flatten().astype(np.float32).tofile(bin_handle) if __name__ == "__main__":