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TSModelForCausalLM support model_type qwen/chatglm #458

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TSModelForCausalLM support model_type qwen/chatglm #458

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74912c0
support qwen
changwangss Oct 19, 2023
548ae9b
Merge branch 'main' into wangchang/qwen
changwangss Nov 9, 2023
34e85a2
record_cache support chatglm
changwangss Nov 9, 2023
f95dea1
Update modeling.py
changwangss Nov 27, 2023
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18 changes: 16 additions & 2 deletions optimum/intel/generation/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -169,6 +169,11 @@ def _reorder_cache(
"""
if self.config.model_type == "bloom":
return self._reorder_cache_bloom(past_key_values, beam_idx)
if self.config.model_type == "chatglm":
return tuple(
tuple(past_state.index_select(1, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past_key_values
)

# from transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel._reorder_cache
return tuple(
Expand Down Expand Up @@ -276,6 +281,8 @@ def forward(
num_attention_heads = self.normalized_config.num_key_value_heads
else:
num_attention_heads = self.normalized_config.num_attention_heads
if model_type in {"chatglm"} and hasattr(self.normalized_config, "multi_query_group_num"):
num_attention_heads = self.normalized_config.multi_query_group_num

if model_type == "bloom":
shape_key = (batch_size * num_attention_heads, d_k, 0)
Expand All @@ -290,15 +297,22 @@ def forward(
pkv = torch.empty(size=shape, dtype=self.model_dtype, device=self._device)
past_key_values = tuple(pkv for _ in range(num_layers))
else:
shape = (batch_size, num_attention_heads, 0, d_k)
if self.config.model_type == "qwen":
shape = (batch_size, 0, num_attention_heads, d_k)
elif self.config.model_type == "chatglm":
shape = (0, batch_size, num_attention_heads, d_k)
else:
shape = (batch_size, num_attention_heads, 0, d_k)
pkv = torch.empty(size=shape, dtype=self.model_dtype, device=self._device)
past_key_values = tuple(tuple(pkv for _ in range(nb_pkv)) for _ in range(num_layers))

inputs["past_key_values"] = past_key_values

if position_ids is not None and model_type in MODEL_TYPES_REQUIRING_POSITION_IDS:
inputs["position_ids"] = position_ids

if position_ids is None and model_type in MODEL_TYPES_REQUIRING_POSITION_IDS:
position_ids = torch.arange(input_ids.shape[1]).repeat(input_ids.shape[0], 1)
inputs["position_ids"] = position_ids
outputs = self.model(**inputs)

if isinstance(outputs, (list, tuple)):
Expand Down