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Fix gpt_bigcode/starcoderbase-3b accuracy issue (#228)

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Co-authored-by: Thanaji Rao Thakkalapelli <tthakkalapelli@habana.ai>
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Sun Choi 2024-10-14 01:01:55 -07:00 committed by GitHub
parent fe8a373831
commit 0578bd917d
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1 changed files with 11 additions and 4 deletions
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15
server/text_generation_server/models/causal_lm.py
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@ -653,7 +653,10 @@ class CausalLM(Model):
self.enable_hpu_graph = os.getenv("ENABLE_HPU_GRAPH", "true").lower() == "true" and LAZY_MODE == 1 self.enable_hpu_graph = os.getenv("ENABLE_HPU_GRAPH", "true").lower() == "true" and LAZY_MODE == 1
self.limit_hpu_graph = os.getenv("LIMIT_HPU_GRAPH", "false").lower() == "true" self.limit_hpu_graph = os.getenv("LIMIT_HPU_GRAPH", "false").lower() == "true"
model = remove_kv_cache_from_output(model)
if model.config.model_type not in ["gpt_bigcode"]: # gpt_bigcode/starcoderbase-3b skips remove_kv_cache_from_output()
model = remove_kv_cache_from_output(model)
if self.enable_hpu_graph: if self.enable_hpu_graph:
from habana_frameworks.torch.hpu import wrap_in_hpu_graph from habana_frameworks.torch.hpu import wrap_in_hpu_graph
model = wrap_in_hpu_graph(model, disable_tensor_cache=True) model = wrap_in_hpu_graph(model, disable_tensor_cache=True)
@ -866,7 +869,7 @@ class CausalLM(Model):
kwargs["bypass_hpu_graphs"] = bypass_hpu_graph kwargs["bypass_hpu_graphs"] = bypass_hpu_graph
kwargs.update(self.kwargs) kwargs.update(self.kwargs)
if past_key_values is not None: if past_key_values is not None and self.model.config.model_type not in ["gpt_bigcode"]:
return self.model.forward(**kwargs) return self.model.forward(**kwargs)
else: else:
outputs = self.model.forward(**kwargs) outputs = self.model.forward(**kwargs)
@ -950,7 +953,7 @@ class CausalLM(Model):
else: else:
batch.position_ids += 1 batch.position_ids += 1
# Update past key values # Update past key values
if prefill: if prefill or self.model.config.model_type in ["gpt_bigcode"]:
batch.past_key_values = past batch.past_key_values = past
htorch.core.mark_step() htorch.core.mark_step()
@ -994,7 +997,7 @@ class CausalLM(Model):
else: else:
token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device) token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device)
input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1) input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1)
batch.logits = self.forward( logits = self.forward(
input_ids, input_ids,
batch.attention_mask, batch.attention_mask,
batch.position_ids, batch.position_ids,
@ -1002,6 +1005,10 @@ class CausalLM(Model):
batch.past_key_values, batch.past_key_values,
bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None, bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None,
) )
if self.model.config.model_type in ["gpt_bigcode"]:
batch.logits, batch.past = logits
else:
batch.logits = logits
htorch.core.mark_step() htorch.core.mark_step()