ceos/unsloth
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update llama.py

Browse source
This commit is contained in:
Daniel Han-Chen 2024-01-21 16:53:58 +11:00
commit 196ab974d6
1 changed files with 17 additions and 18 deletions
Download patch file Download diff file Expand all files Collapse all files

55
unsloth/models/llama.py
View file

@ -305,29 +305,7 @@ def LlamaDecoderLayer_fast_forward(
"""
bsz, q_len, hd = hidden_states.size()
if (self.training):
# Self Attention
residual = hidden_states
hidden_states = fast_rms_layernorm(self.input_layernorm, hidden_states)
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
causal_mask=causal_mask,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
padding_mask=padding_mask,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = fast_rms_layernorm(self.post_attention_layernorm, hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
else:
print(hidden_states.shape)
if (not self.training and bsz == 1):
# Self Attention
residual = hidden_states
hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
@ -348,6 +326,26 @@ def LlamaDecoderLayer_fast_forward(
hidden_states = fast_rms_layernorm_inference(self.post_attention_layernorm, hidden_states)
hidden_states = fast_mlp_inference(self.mlp, hidden_states)
hidden_states += residual
else:
residual = hidden_states
hidden_states = fast_rms_layernorm(self.input_layernorm, hidden_states)
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
causal_mask=causal_mask,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
padding_mask=padding_mask,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = fast_rms_layernorm(self.post_attention_layernorm, hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
pass
outputs = (hidden_states,)
@ -446,7 +444,7 @@ def LlamaModel_fast_forward(
# Ignore attention_mask
if attention_mask is None:
padding_mask = None
elif self.training:
elif True:#self.training:
attention_mask = None
padding_mask = None
else:
@ -524,11 +522,12 @@ def LlamaModel_fast_forward(
if output_attentions:
all_self_attns += (layer_outputs[1],)
pass
if (self.training):
hidden_states = fast_rms_layernorm(self.norm, hidden_states)
else:
bsz, q_len, hd = hidden_states.size()
if (not self.training and q_len == 1):
hidden_states = fast_rms_layernorm_inference(self.norm, hidden_states)
else:
hidden_states = fast_rms_layernorm(self.norm, hidden_states)
pass
# add hidden states from the last decoder layer