Delete convert_weight.py

convert_weight.py

@@ -1,72 +0,0 @@
-import torch
-
-
-input_dir_path = "/scratch/project_462000086/norwegian_gpt/Megatron-DeepSpeed-fixed/checkpoints/global_step120000"
-output_dir_path = "/scratch/project_462000086/norwegian_gpt/Megatron-DeepSpeed-fixed/hf_pilot_checkpoint_120k"
-
-n_hidden = 4096
-n_heads = 32
-n_layers = 32
-n_tp = 4
-
-
-weights = {}
-
-# embedding
-embedding_weights = []
-for i in range(n_tp):
-    path = f"{input_dir_path}/layer_01-model_0{i}-model_states.pt"
-    checkpoint = torch.load(path)
-
-    embedding_weights.append(checkpoint["word_embeddings.weight"].bfloat16())
-
-    weights[f"transformer.word_embeddings_layernorm.weight"] = checkpoint["word_embeddings.norm.weight"].bfloat16()
-    weights[f"transformer.word_embeddings_layernorm.bias"] = checkpoint["word_embeddings.norm.bias"].bfloat16()
-
-weights[f"transformer.word_embeddings.weight"] = torch.cat(embedding_weights, dim=0)
-weights[f"lm_head.weight"] = torch.cat(embedding_weights, dim=0)
-del embedding_weights
-
-
-# transformer layers
-for layer in range(n_layers):
-    qkv_weights = []
-    qkv_biases = []
-    o_weights = []
-    up_weights = []
-    up_biases = []
-    down_weights = []
-
-    for i in range(n_tp):
-        path = f"{input_dir_path}/layer_{layer+3:02d}-model_0{i}-model_states.pt"
-        checkpoint = torch.load(path)
-
-        weights[f"transformer.h.{layer}.input_layernorm.weight"] = checkpoint["input_layernorm.weight"].bfloat16()
-        weights[f"transformer.h.{layer}.input_layernorm.bias"] = checkpoint["input_layernorm.bias"].bfloat16()
-        weights[f"transformer.h.{layer}.self_attention.dense.bias"] = checkpoint["self_attention.dense.bias"].bfloat16()
-        weights[f"transformer.h.{layer}.post_attention_layernorm.weight"] = checkpoint["post_attention_layernorm.weight"].bfloat16()
-        weights[f"transformer.h.{layer}.post_attention_layernorm.bias"] = checkpoint["post_attention_layernorm.bias"].bfloat16()
-        weights[f"transformer.h.{layer}.mlp.dense_4h_to_h.bias"] = checkpoint["mlp.dense_4h_to_h.bias"].bfloat16()
-
-        qkv_weights.append(checkpoint["self_attention.query_key_value.weight"].bfloat16())
-        qkv_biases.append(checkpoint["self_attention.query_key_value.bias"].bfloat16())
-        o_weights.append(checkpoint["self_attention.dense.weight"].bfloat16())
-        up_weights.append(checkpoint["mlp.dense_h_to_4h.weight"].bfloat16())
-        up_biases.append(checkpoint["mlp.dense_h_to_4h.bias"].bfloat16())
-        down_weights.append(checkpoint["mlp.dense_4h_to_h.weight"].bfloat16())
-
-    weights[f"transformer.h.{layer}.self_attention.query_key_value.weight"] = torch.cat(qkv_weights, dim=0)
-    weights[f"transformer.h.{layer}.self_attention.query_key_value.bias"] = torch.cat(qkv_biases, dim=0)
-    weights[f"transformer.h.{layer}.self_attention.dense.weight"] = torch.cat(o_weights, dim=1)
-    weights[f"transformer.h.{layer}.mlp.dense_h_to_4h.weight"] = torch.cat(up_weights, dim=0)
-    weights[f"transformer.h.{layer}.mlp.dense_h_to_4h.bias"] = torch.cat(up_biases, dim=0)
-    weights[f"transformer.h.{layer}.mlp.dense_4h_to_h.weight"] = torch.cat(down_weights, dim=1)
-
-# output layer norm
-path = f"{input_dir_path}/layer_36-model_00-model_states.pt"
-checkpoint = torch.load(path)
-
-weights[f"transformer.ln_f.bias"] = checkpoint["bias"].bfloat16()
-weights[f"transformer.ln_f.weight"] = checkpoint["weight"].bfloat16()
-
-torch.save(weights, f"{output_dir_path}/pytorch_model.bin")