Create convert_biomegatron_checkpoint.py

convert_biomegatron_checkpoint.py

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+import argparse
+import json
+import os
+import re
+import zipfile
+
+import torch
+
+####################################################################################################
+# This file is a modification of the original
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py
+
+def recursive_print(name, val, spaces=0):
+    # Format the message.
+    if name is None:
+        msg = None
+    else:
+        fmt = "." * max(0, spaces - 2) + "# {:" + str(50 - spaces) + "s}"
+        msg = fmt.format(name)
+
+    # Print and recurse (if needed).
+    if isinstance(val, dict):
+        if msg is not None:
+            print(msg)
+        for k in val.keys():
+            recursive_print(k, val[k], spaces + 2)
+    elif isinstance(val, torch.Tensor):
+        print(msg, ":", val.size())
+    else:
+        print(msg, ":", val)
+
+        
+def convert_megatron_checkpoint(input_state_dict, head_model=True):
+    # The converted output model.
+    output_state_dict = {}
+
+    # The model.
+    model = input_state_dict["model"]
+    # The language model.
+    lm = model["language_model"]
+    # The embeddings.
+    embeddings = lm["embedding"]
+
+    # The word embeddings.
+    word_embeddings = embeddings["word_embeddings"]["weight"]
+    # Store the word embeddings.
+    output_state_dict["bert.embeddings.word_embeddings.weight"] = word_embeddings
+
+    # The position embeddings.
+    pos_embeddings = embeddings["position_embeddings"]["weight"]
+    # Trained for 512 x 1024.
+    assert pos_embeddings.size(0) == 512 and pos_embeddings.size(1) == 1024
+    # Store the position embeddings.
+    output_state_dict["bert.embeddings.position_embeddings.weight"] = pos_embeddings
+
+    # The token-type embeddings.
+    tokentype_embeddings = embeddings["tokentype_embeddings"]["weight"]
+    # Store the position embeddings.
+    output_state_dict["bert.embeddings.token_type_embeddings.weight"] = tokentype_embeddings
+
+    # The transformer.
+    transformer = lm["transformer"]
+
+    # The regex to extract layer names.
+    layer_re = re.compile("layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")
+
+    # The simple map of names for "automated" rules.
+    megatron_to_transformers = {
+        "attention.dense": ".attention.output.dense.",
+        "mlp.dense_h_to_4h": ".intermediate.dense.",
+        "mlp.dense_4h_to_h": ".output.dense.",
+    }
+
+    # Keep track of the attention/query/value tensor.
+    attention_qkv_weight = None
+
+    # Extract the layers.
+    for key, val in transformer.items():
+        # Match the name.
+        m = layer_re.match(key)
+
+        # Stop if that's not a layer
+        if m is None:
+            break
+
+        # The index of the layer.
+        layer_idx = int(m.group(1))
+        # The name of the operation.
+        op_name = m.group(2)
+        # Is it a weight or a bias?
+        weight_or_bias = m.group(3)
+
+        # The name of the layer.
+        layer_name = f"bert.encoder.layer.{layer_idx}"
+
+        # For layernorm(s), simply store the layer norm.
+        if op_name.endswith("layernorm"):
+
+            ln_name = "attention.ln" if op_name.startswith("input") else "ln"
+            output_state_dict[layer_name + "." + ln_name + "." + weight_or_bias] = val
+
+        # Transpose the QKV matrix.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "weight":
+
+            # Make sure the QKV pointer is nil.
+            assert attention_qkv_weight is None, ""
+
+            # Store the tensor as we need the bias as well to interleave QKV and biases.
+            attention_qkv_weight = val
+
+        # Transpose the bias.
+        elif op_name == "attention.query_key_value" and weight_or_bias == "bias":
+
+            # Make sure we read the weight tensor.
+            assert attention_qkv_weight is not None, ""
+
+            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
+            q = attention_qkv_weight[0 * 1024 : 1 * 1024, :]
+            k = attention_qkv_weight[1 * 1024 : 2 * 1024, :]
+            v = attention_qkv_weight[2 * 1024 : 3 * 1024, :]
+
+            # Split the bias.
+            q_bias = val[0 * 1024 : 1 * 1024]
+            k_bias = val[1 * 1024 : 2 * 1024]
+            v_bias = val[2 * 1024 : 3 * 1024]
+
+            # Store.
+            output_state_dict[f"{layer_name}.attention.self.query.weight"] = q
+            output_state_dict[f"{layer_name}.attention.self.query.bias"] = q_bias
+            output_state_dict[f"{layer_name}.attention.self.key.weight"] = k
+            output_state_dict[f"{layer_name}.attention.self.key.bias"] = k_bias
+            output_state_dict[f"{layer_name}.attention.self.value.weight"] = v
+            output_state_dict[f"{layer_name}.attention.self.value.bias"] = v_bias
+
+            # Clear the stored tensor.
+            attention_qkv_weight = None
+
+        # Copy weights and biases as is.
+        elif weight_or_bias in ["weight", "bias"]:
+
+            out_name = megatron_to_transformers[op_name]
+            output_state_dict[layer_name + out_name + weight_or_bias] = val
+
+    # The final layernorm.
+    output_state_dict["bert.encoder.ln.weight"] = transformer["final_layernorm.weight"]
+    output_state_dict["bert.encoder.ln.bias"] = transformer["final_layernorm.bias"]
+
+    # The config.
+    output_config = {
+        "vocab_size": word_embeddings.size(0),
+        "hidden_size": 1024,
+        "num_hidden_layers": 24,
+        "num_attention_heads": 16,
+        "hidden_act": "gelu_new",
+        "intermediate_size": 4096,
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "initializer_range": 0.2,
+        "layer_norm_eps": 1e-12,
+        "position_embedding_type": "absolute",
+        "use_cache": False,
+        "model_type": "megatron-bert",
+    }
+
+    if head_model:
+        # The pooler.
+        pooler = lm["pooler"]
+
+        # Store the matrix and the bias.
+        output_state_dict["bert.pooler.dense.weight"] = pooler["dense.weight"]
+        output_state_dict["bert.pooler.dense.bias"] = pooler["dense.bias"]
+
+        # The LM head from Megatron (for RACE).
+        lm_head = model["lm_head"]
+
+        # The transform matrix.
+        output_state_dict["cls.predictions.transform.dense.weight"] = lm_head["dense.weight"]
+        output_state_dict["cls.predictions.transform.dense.bias"] = lm_head["dense.bias"]
+
+        # The transform LN.
+        output_state_dict["cls.predictions.transform.LayerNorm.weight"] = lm_head["layernorm.weight"]
+        output_state_dict["cls.predictions.transform.LayerNorm.bias"] = lm_head["layernorm.bias"]
+
+        # For the decoder, we replicate the weights.
+        output_state_dict["cls.predictions.decoder.weight"] = word_embeddings
+        output_state_dict["cls.predictions.bias"] = lm_head["bias"]
+
+        # The classifier from Megatron (for MLNI).
+        binary_head = model["binary_head"]
+
+        # Store the classifier.
+        output_state_dict["cls.seq_relationship.weight"] = binary_head["weight"]
+        output_state_dict["cls.seq_relationship.bias"] = binary_head["bias"]
+
+    # It should be done!
+    return output_state_dict, output_config