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Added missing allocate_kv_cache() call in CausalLM class #1431

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Merged
regisss merged 2 commits into from
Oct 18, 2024
Merged

Added missing allocate_kv_cache() call in CausalLM class #1431

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e34f281
Added missing allocate_kv_cache() call in CausalLM class
yeonsily Oct 16, 2024
9c0abea
Remove duplacated code and update modeling_gemma to use GaudiRotaryEm…
yeonsily Oct 17, 2024
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2 changes: 0 additions & 2 deletions optimum/habana/transformers/generation/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,14 +99,12 @@
"starcoder2",
"persimmon",
"qwen2",
"starcoder2",
"llava",
"llava_next",
"stablelm",
"mamba",
"deci",
"qwen2_moe",
"gemma",
"whisper",
]

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17 changes: 14 additions & 3 deletions optimum/habana/transformers/models/gemma/modeling_gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@
from ...modeling_attn_mask_utils import (
_gaudi_prepare_4d_causal_attention_mask,
)
from ...modeling_rope_utils import GaudiRotaryEmbedding


try:
Expand Down Expand Up @@ -141,6 +142,7 @@ def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
self.inp_seq_len = -1
self.norm_factor = 1.0 / math.sqrt(self.head_dim)
self.block_size = 4096
self.rotary_emb = GaudiRotaryEmbedding(config=self.config)

def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
cache_shape = (batch_size, self.num_key_value_heads, max_seq_len, self.head_dim)
Expand All @@ -155,7 +157,7 @@ def update_sincos_cache(self, seq_len):
# reduce memory consumption and improve performance.
if seq_len > self.max_position_embeddings:
self.max_position_embeddings = seq_len
_, _ = self.rotary_emb(self.k_proj.weight, seq_len=seq_len)
self.rotary_emb._set_cos_sin_cache(seq_len, self.k_proj.weight.device, self.k_proj.weight.dtype)

def reorder(self, tensor, beam_idx, dim_a, dim_b):
updated = tensor.index_select(0, beam_idx)
Expand Down Expand Up @@ -252,8 +254,8 @@ def pre_attn_forward(
else:
kv_seq_len = past_key_value[0].shape[-2]

cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos[position_ids], sin[position_ids])

if use_cache:
# reuse k, v, self_attention
Expand Down Expand Up @@ -697,6 +699,15 @@ def forward(


class GaudiGemmaForCausalLM(GemmaForCausalLM):
def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
self.model.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

def reorder_kv_cache(self, beam_idx: torch.LongTensor):
return self.model.reorder_kv_cache(beam_idx)

def update_sincos_cache(self, seq_len):
self.model.update_sincos_cache(seq_len)

def forward(
self,
input_ids: torch.LongTensor = None,
Expand Down