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rename layers

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lakshith
2024-08-18 01:04:04 +05:30
parent f3465ac926
commit 863772e04a
2 changed files with 21 additions and 23 deletions
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24
labml_nn/lora/experiment.py
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@ -76,16 +76,16 @@ class Trainer(BaseConfigs):
for i in range(12):
mapping[f'transformer.h.{i}.ln_1.weight'] = f'blocks.{i}.pre_norm.weight'
mapping[f'transformer.h.{i}.ln_1.bias'] = f'blocks.{i}.pre_norm.bias'
mapping[f'transformer.h.{i}.attn.c_attn.weight'] = f'blocks.{i}.attn.c_att.weight'
mapping[f'transformer.h.{i}.attn.c_attn.bias'] = f'blocks.{i}.attn.c_att.bias'
mapping[f'transformer.h.{i}.attn.c_proj.weight'] = f'blocks.{i}.attn.c_proj.weight'
mapping[f'transformer.h.{i}.attn.c_proj.bias'] = f'blocks.{i}.attn.c_proj.bias'
mapping[f'transformer.h.{i}.attn.c_attn.weight'] = f'blocks.{i}.attn.qkv_projection.weight'
mapping[f'transformer.h.{i}.attn.c_attn.bias'] = f'blocks.{i}.attn.qkv_projection.bias'
mapping[f'transformer.h.{i}.attn.c_proj.weight'] = f'blocks.{i}.attn.output_projection.weight'
mapping[f'transformer.h.{i}.attn.c_proj.bias'] = f'blocks.{i}.attn.output_projection.bias'
mapping[f'transformer.h.{i}.ln_2.weight'] = f'blocks.{i}.post_norm.weight'
mapping[f'transformer.h.{i}.ln_2.bias'] = f'blocks.{i}.post_norm.bias'
mapping[f'transformer.h.{i}.mlp.c_fc.weight'] = f'blocks.{i}.ffn.c_fc.weight'
mapping[f'transformer.h.{i}.mlp.c_fc.bias'] = f'blocks.{i}.ffn.c_fc.bias'
mapping[f'transformer.h.{i}.mlp.c_proj.weight'] = f'blocks.{i}.ffn.c_proj.weight'
mapping[f'transformer.h.{i}.mlp.c_proj.bias'] = f'blocks.{i}.ffn.c_proj.bias'
mapping[f'transformer.h.{i}.mlp.c_fc.weight'] = f'blocks.{i}.ffn.linear_in.weight'
mapping[f'transformer.h.{i}.mlp.c_fc.bias'] = f'blocks.{i}.ffn.linear_in.bias'
mapping[f'transformer.h.{i}.mlp.c_proj.weight'] = f'blocks.{i}.ffn.linear_out.weight'
mapping[f'transformer.h.{i}.mlp.c_proj.bias'] = f'blocks.{i}.ffn.linear_out.bias'
# Move the parameters based on mapping
new_state_dict = {}
@ -94,10 +94,10 @@ class Trainer(BaseConfigs):
new_state_dict[new_key] = state_dict[old_key]
# GPT-2 hugging face uses 1D Convolution layers. We need to transpose those weights since we use linear layers
convo_layers = ([f'blocks.{i}.ffn.c_fc.weight' for i in range(12)] +
[f'blocks.{i}.ffn.c_proj.weight' for i in range(12)] +
[f'blocks.{i}.attn.c_att.weight' for i in range(12)] +
[f'blocks.{i}.attn.c_proj.weight' for i in range(12)])
convo_layers = ([f'blocks.{i}.ffn.linear_in.weight' for i in range(12)] +
[f'blocks.{i}.ffn.linear_out.weight' for i in range(12)] +
[f'blocks.{i}.attn.qkv_projection.weight' for i in range(12)] +
[f'blocks.{i}.attn.output_projection.weight' for i in range(12)])
for layer in convo_layers:
new_state_dict[layer] = torch.transpose(new_state_dict[layer], 0, 1)

20
labml_nn/lora/gpt2.py
View File

@ -6,16 +6,14 @@ from labml_nn.lora import Linear, Embedding
class FFN(nn.Module):
def __init__(self, dim: int, n_embed: int, r: int):
super().__init__()
# lin1
self.c_fc = Linear(n_embed, dim, r=r, bias=True)
# lin2
self.c_proj = Linear(dim, n_embed, r=r, bias=True)
self.linear_in = Linear(n_embed, dim, r=r, bias=True)
self.linear_out = Linear(dim, n_embed, r=r, bias=True)
self.act = nn.functional.gelu
def forward(self, hidden_states):
hidden_states = self.c_fc(hidden_states)
hidden_states = self.linear_in(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.c_proj(hidden_states)
hidden_states = self.linear_out(hidden_states)
return hidden_states
@ -27,10 +25,10 @@ class MultiHeadAttention(nn.Module):
self.head_dim = self.embed_dim // self.num_heads
self.split_size = self.embed_dim
# qkv
self.c_att = Linear(n_embed, n_embed * 3, r=r, bias=True)
# query key value
self.qkv_projection = Linear(n_embed, n_embed * 3, r=r, bias=True)
# out
self.c_proj = Linear(n_embed, n_embed, r=r, bias=True)
self.output_projection = Linear(n_embed, n_embed, r=r, bias=True)
def _split_heads(self, tensor, num_heads, attn_head_size):
"""
@ -43,7 +41,7 @@ class MultiHeadAttention(nn.Module):
def forward(self, hidden_states):
batch_size, seq_length, _ = hidden_states.size()
query, key, value = self.c_att(hidden_states).split(self.split_size, dim=2)
query, key, value = self.qkv_projection(hidden_states).split(self.split_size, dim=2)
query = self._split_heads(query, self.num_heads, self.head_dim)
key = self._split_heads(key, self.num_heads, self.head_dim)
@ -61,7 +59,7 @@ class MultiHeadAttention(nn.Module):
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, seq_length, self.embed_dim)
attn_output = self.c_proj(attn_output)
attn_output = self.output_projection(attn_output)
return attn_output