initial (wip) convert_weights script from pytorch

util/convert_weights.py

@@ -0,0 +1,55 @@
+# WIP - DO NOT MERGE
+
+import torch
+from gemma import config
+from gemma import model as gemma_model
+import numpy as np
+
+
+def param_names():
+    """Return parameter names in the order they are expected for deserialization."""
+    names = ["embedder.weight", "model.norm.weight"]
+    # 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
+    layer_params = [
+        "self_attn.qkv_proj.weight",  # attn_vec_einsum_w
+        "self_attn.o_proj.weight",  # qkv_einsum_w
+        "mlp.gate_proj.weight",  # qkv_einsum_w
+        "mlp.up_proj.weight",  # gating_einsum_w
+        "mlp.down_proj.weight",  # linear_w
+        "input_layernorm.weight",  # pre_attention_norm_scale
+        "post_attention_layernorm.weight",  # pre_ffw_norm_scale
+    ]
+    for layer in range(18):
+        for layer_param in layer_params:
+            names = names + ["model.layers." + str(layer) + "." + layer_param]
+    return names
+
+
+def convert_weights():
+    # TODO(austinvhuang): move code in here
+    pass
+
+
+if __name__ == "__main__":
+    # TODO(austinvhuang): parameterize paths
+    output_file = "2bit-f32.sbs"
+    model_config = config.get_model_config("2b")
+    model_config.dtype = "float32"
+    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())
+    param_order = param_names()
+    print("Writing parameters ...")
+    with open(output_file, "wb") as bin_handle:
+        for name in param_order:
+            arr = model_dict[name].detach().numpy()
+            # TODO(austinvhuang): reshapes
+            print(f"  {name : <60}{str(arr.shape)}")
+            arr.flatten().astype(np.float32).tofile(bin_handle)
+
+    print("Done")