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adds llama and mistral dropout support (#858)

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* adds llama and mistral dropout support

* gracefully handle attention dropout if not available yet
This commit is contained in:
Wing Lian
2023-11-15 12:28:50 -05:00
committed by GitHub
parent 14706504e3
commit db8a8afcba
2 changed files with 17 additions and 6 deletions
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14
src/axolotl/monkeypatch/llama_attn_hijack_flash.py
View File

@@ -321,6 +321,8 @@ def flashattn_forward(
# only on first autoregressive step q,k,v have same seqlen
is_causal = key_states.shape == query_states.shape
dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
# special handling using sample packing
qkv = torch.stack(
@@ -330,7 +332,12 @@ def flashattn_forward(
qkv = rearrange(qkv, "b s ... -> (b s) ...")
output = flash_attn_varlen_qkvpacked_func(
qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
qkv,
cu_seqlens,
max_seqlen,
dropout_p=dropout_rate,
softmax_scale=None,
causal=True,
)
output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
elif query_states.shape == key_states.shape:
@@ -353,7 +360,7 @@ def flashattn_forward(
qkv_unpad,
cu_seqlens_q,
max_seqlen_q,
0.0,
dropout_p=dropout_rate,
softmax_scale=None,
causal=is_causal,
)
@@ -366,6 +373,7 @@ def flashattn_forward(
output = flash_attn_kvpacked_func(
query_states,
torch.stack([key_states, value_states], 2),
dropout_p=dropout_rate,
causal=is_causal,
)
else:
@@ -398,7 +406,7 @@ def flashattn_forward(
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
0.0,
dropout_p=dropout_rate,
softmax_scale=None,
causal=is_causal,
)

9
src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
View File

@@ -201,6 +201,8 @@ def flashattn_forward(
# only on first autoregressive step q,k,v have same seqlen
is_causal = key_states.shape == query_states.shape
dropout_rate = 0.0 if not self.training else getattr(self, "attention_dropout", 0.0)
if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
# special handling using sample packing
qkv = torch.stack(
@@ -213,7 +215,7 @@ def flashattn_forward(
qkv,
cu_seqlens,
max_seqlen,
0.0,
dropout_p=dropout_rate,
softmax_scale=None,
causal=True,
window_size=window_size,
@@ -239,7 +241,7 @@ def flashattn_forward(
qkv_unpad,
cu_seqlens_q,
max_seqlen_q,
0.0,
dropout_p=dropout_rate,
softmax_scale=None,
causal=is_causal,
window_size=window_size,
@@ -253,6 +255,7 @@ def flashattn_forward(
output = flash_attn_kvpacked_func(
query_states,
torch.stack([key_states, value_states], 2),
dropout_p=dropout_rate,
causal=is_causal,
window_size=window_size,
)
@@ -286,7 +289,7 @@ def flashattn_forward(
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
0.0,
dropout_p=dropout_rate,
softmax_scale=None,
causal=is_causal,
window_size=window_size,