File size: 4,731 Bytes
16b7417 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import torch
from torch import nn
from transformers import HubertConfig, HubertModel
import logging
# Ignore fairseq's logger
logging.getLogger("fairseq").setLevel(logging.WARNING)
logging.getLogger("torch.distributed.nn.jit.instantiator").setLevel(logging.WARNING)
from fairseq import checkpoint_utils
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
["content-vec-best-legacy-500.pt"], suffix=""
)
model = models[0]
model.eval()
model.eval()
class HubertModelWithFinalProj(HubertModel):
def __init__(self, config):
super().__init__(config)
self.final_proj = nn.Linear(config.hidden_size, config.classifier_proj_size)
# Default Config
hubert = HubertModelWithFinalProj(HubertConfig())
# huggingface: fairseq
mapping = {
"masked_spec_embed": "mask_emb",
"encoder.layer_norm.bias": "encoder.layer_norm.bias",
"encoder.layer_norm.weight": "encoder.layer_norm.weight",
"encoder.pos_conv_embed.conv.bias": "encoder.pos_conv.0.bias",
"encoder.pos_conv_embed.conv.weight_g": "encoder.pos_conv.0.weight_g",
"encoder.pos_conv_embed.conv.weight_v": "encoder.pos_conv.0.weight_v",
"feature_projection.layer_norm.bias": "layer_norm.bias",
"feature_projection.layer_norm.weight": "layer_norm.weight",
"feature_projection.projection.bias": "post_extract_proj.bias",
"feature_projection.projection.weight": "post_extract_proj.weight",
"final_proj.bias": "final_proj.bias",
"final_proj.weight": "final_proj.weight",
}
# Convert encoder
for layer in range(12):
for j in ["q", "k", "v"]:
mapping[
f"encoder.layers.{layer}.attention.{j}_proj.weight"
] = f"encoder.layers.{layer}.self_attn.{j}_proj.weight"
mapping[
f"encoder.layers.{layer}.attention.{j}_proj.bias"
] = f"encoder.layers.{layer}.self_attn.{j}_proj.bias"
mapping[
f"encoder.layers.{layer}.final_layer_norm.bias"
] = f"encoder.layers.{layer}.final_layer_norm.bias"
mapping[
f"encoder.layers.{layer}.final_layer_norm.weight"
] = f"encoder.layers.{layer}.final_layer_norm.weight"
mapping[
f"encoder.layers.{layer}.layer_norm.bias"
] = f"encoder.layers.{layer}.self_attn_layer_norm.bias"
mapping[
f"encoder.layers.{layer}.layer_norm.weight"
] = f"encoder.layers.{layer}.self_attn_layer_norm.weight"
mapping[
f"encoder.layers.{layer}.attention.out_proj.bias"
] = f"encoder.layers.{layer}.self_attn.out_proj.bias"
mapping[
f"encoder.layers.{layer}.attention.out_proj.weight"
] = f"encoder.layers.{layer}.self_attn.out_proj.weight"
mapping[
f"encoder.layers.{layer}.feed_forward.intermediate_dense.bias"
] = f"encoder.layers.{layer}.fc1.bias"
mapping[
f"encoder.layers.{layer}.feed_forward.intermediate_dense.weight"
] = f"encoder.layers.{layer}.fc1.weight"
mapping[
f"encoder.layers.{layer}.feed_forward.output_dense.bias"
] = f"encoder.layers.{layer}.fc2.bias"
mapping[
f"encoder.layers.{layer}.feed_forward.output_dense.weight"
] = f"encoder.layers.{layer}.fc2.weight"
# Convert Conv Layers
for layer in range(7):
mapping[
f"feature_extractor.conv_layers.{layer}.conv.weight"
] = f"feature_extractor.conv_layers.{layer}.0.weight"
if layer != 0:
continue
mapping[
f"feature_extractor.conv_layers.{layer}.layer_norm.weight"
] = f"feature_extractor.conv_layers.{layer}.2.weight"
mapping[
f"feature_extractor.conv_layers.{layer}.layer_norm.bias"
] = f"feature_extractor.conv_layers.{layer}.2.bias"
hf_keys = set(hubert.state_dict().keys())
fair_keys = set(model.state_dict().keys())
hf_keys -= set(mapping.keys())
fair_keys -= set(mapping.values())
for i, j in zip(sorted(hf_keys), sorted(fair_keys)):
print(i, j)
print(hf_keys, fair_keys)
print(len(hf_keys), len(fair_keys))
# try loading the weights
new_state_dict = {}
for k, v in mapping.items():
new_state_dict[k] = model.state_dict()[v]
x = hubert.load_state_dict(new_state_dict, strict=False)
print(x)
hubert.eval()
with torch.no_grad():
new_input = torch.randn(1, 16384)
result1 = hubert(new_input, output_hidden_states=True)["hidden_states"][9]
result1 = hubert.final_proj(result1)
result2 = model.extract_features(
**{
"source": new_input,
"padding_mask": torch.zeros(1, 16384, dtype=torch.bool),
# "features_only": True,
"output_layer": 9,
}
)[0]
result2 = model.final_proj(result2)
assert torch.allclose(result1, result2, atol=1e-3)
print("Sanity check passed")
# Save huggingface model
hubert.save_pretrained(".")
print("Saved model")
|