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Encapsulate FSDPA in GaudiLlamaAttention #129

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Merged
MrGeva merged 1 commit into from
Mar 24, 2024
Merged

Encapsulate FSDPA in GaudiLlamaAttention #129

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2 changes: 2 additions & 0 deletions examples/text-generation/run_lm_eval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,8 @@ def __init__(self, tokenizer, model, args, options):
self.model_inputs.update(
{
"attn_softmax_bf16": self.options.attn_softmax_bf16,
"use_flash_attention": self.options.use_flash_attention,
"flash_attention_recompute": self.options.flash_attention_recompute,
}
)
if args.warmup:
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19 changes: 16 additions & 3 deletions optimum/habana/transformers/models/llama/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,6 +107,16 @@ def gaudi_llama_repeat_kv(
return query_states, key_states, value_states, attention_mask


# FusedScaledDotProductAttention
class ModuleFusedSDPA(torch.nn.Module):
def __init__(self, fusedSDPA):
super().__init__()
self._hpu_kernel_fsdpa = fusedSDPA

def forward(self, query, key, value, attn_mask, dropout_p, is_casual, scale):
return self._hpu_kernel_fsdpa.apply(query, key, value, attn_mask, dropout_p, is_casual, scale)


class Matmul(torch.nn.Module):
def __init__(self):
super().__init__()
Expand Down Expand Up @@ -164,6 +174,7 @@ def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
self.matmul_av = Matmul()
self.k_cache = KVCache()
self.v_cache = KVCache()
self.fused_scaled_dot_product_attention = ModuleFusedSDPA(FusedSDPA) if FusedSDPA else None
self.inp_seq_len = -1
self.norm_factor = 1.0 / math.sqrt(self.head_dim)

Expand Down Expand Up @@ -309,18 +320,20 @@ def pre_attn_forward(
if q_len == 1:
# next token
with ht.sdp_kernel(enable_recompute=False):
attn_output = FusedSDPA.apply(
attn_output = self.fused_scaled_dot_product_attention(
query_states, key_states, value_states, attention_mask, 0.0, False, None
)
else:
# first token
if flash_attention_causal_mask:
# causal masking on first token requires inputs to be of the same lenght
with ht.sdp_kernel(enable_recompute=flash_attention_recompute):
attn_output = FusedSDPA.apply(query_states, key_states, value_states, None, 0.0, True, None)
attn_output = self.fused_scaled_dot_product_attention(
query_states, key_states, value_states, None, 0.0, True, None
)
else:
with ht.sdp_kernel(enable_recompute=flash_attention_recompute):
attn_output = FusedSDPA.apply(
attn_output = self.fused_scaled_dot_product_attention(
query_states, key_states, value_states, attention_mask, 0.0, False, None
)

Expand Down