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Merge pull request #68 from mayank31398/mayank/fp32-prompt-tuning

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convert prompt tuning vocab to fp32
This commit is contained in:
Sourab Mangrulkar
2023-02-13 11:26:46 +05:30
committed by GitHub
parent 5acd392880 29d608f481
commit be0e79c271
2 changed files with 8 additions and 3 deletions
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src/peft/peft_model.py
+7 -3
View File
@@ -384,6 +384,7 @@ class PeftModelForSequenceClassification(PeftModel):
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
@@ -542,6 +543,7 @@ class PeftModelForCausalLM(PeftModel):
prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device)
kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
@@ -577,10 +579,10 @@ class PeftModelForCausalLM(PeftModel):
model_kwargs["past_key_values"] = past_key_values
else:
if model_kwargs["past_key_values"] is None:
inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
model_kwargs["inputs_embeds"] = torch.cat(
(prompts, self.word_embeddings(model_kwargs["input_ids"])), dim=1
)
prompts = prompts.to(inputs_embeds.dtype)
model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
model_kwargs["input_ids"] = None
return model_kwargs
@@ -694,6 +696,7 @@ class PeftModelForSeq2SeqLM(PeftModel):
prefix_labels = torch.full((batch_size, self.peft_config.num_virtual_tokens), -100).to(self.device)
kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts[:, : self.peft_config.num_virtual_tokens], inputs_embeds), dim=1)
decoder_inputs_embeds = torch.cat(
(prompts[:, self.peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1
@@ -824,6 +827,7 @@ class PeftModelForTokenClassification(PeftModel):
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
prompts = self.get_prompt(batch_size=batch_size)
prompts = prompts.to(inputs_embeds.dtype)
inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
src/peft/tuners/prompt_tuning.py
+1
View File
@@ -111,6 +111,7 @@ class PromptEmbedding(torch.nn.Module):
init_token_ids = init_token_ids[:total_virtual_tokens]
word_embedding_weights = word_embeddings(torch.LongTensor(init_token_ids)).detach().clone()
word_embedding_weights = word_embedding_weights.to(torch.float32)
self.embedding.weight = torch.nn.Parameter(word_embedding_weights)
def forward(self, indices):