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Update stream llm to get correct outputs and re-enable rerotated-attention test. #656

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Apr 25, 2024
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Update stream llm to get correct outputs and re-enable rerotated-attention test. #656

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7294808
Update streaming-llm attention impl
raikonenfnu Apr 25, 2024
3df91b5
Update/fix streaming llm attention to use decomposed ops (good output)
raikonenfnu Apr 25, 2024
b733ac0
Remove experiment fluff + re-enable tests.
raikonenfnu Apr 25, 2024
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82 changes: 25 additions & 57 deletions models/turbine_models/custom_models/llm_optimizations/streaming_llm/modify_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,38 +37,9 @@ def llama_pos_shift_attention_forward(
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()

if self.config.pretraining_tp > 1:
key_value_slicing = (
self.num_key_value_heads * self.head_dim
) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

query_states = [
F.linear(hidden_states, query_slices[i])
for i in range(self.config.pretraining_tp)
]
query_states = torch.cat(query_states, dim=-1)

key_states = [
F.linear(hidden_states, key_slices[i])
for i in range(self.config.pretraining_tp)
]
key_states = torch.cat(key_states, dim=-1)

value_states = [
F.linear(hidden_states, value_slices[i])
for i in range(self.config.pretraining_tp)
]
value_states = torch.cat(value_states, dim=-1)

else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)

query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
Expand Down Expand Up @@ -103,9 +74,9 @@ def llama_pos_shift_attention_forward(
# repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)

attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(
self.head_dim
softmax_scale = 1.0 / math.sqrt(self.head_dim)
attn_weights = (
torch.matmul(query_states, key_states.transpose(2, 3)) * softmax_scale
)

if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
Expand All @@ -114,6 +85,23 @@ def llama_pos_shift_attention_forward(
f" {attn_weights.size()}"
)

# For causal mode, we use to get input mask, but now causal mode does not expect a mask
# and we need to generate the causal mask ourselves.
current_is_causal = False
if self.is_causal and attention_mask is None and q_len > 1:
current_is_causal = True
if current_is_causal and attention_mask is None:
bool_attention_mask = torch.ones(
[query_states.shape[-2], key_states.shape[-2]],
device=query_states.device,
dtype=torch.bool,
).tril()
additive_attention_mask = torch.zeros_like(
bool_attention_mask, dtype=attn_weights.dtype
).masked_fill(bool_attention_mask.logical_not(), -10000)
attn_weights = attn_weights + additive_attention_mask

# Legacy support to take in mask for non-causal mode.
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
Expand All @@ -132,30 +120,10 @@ def llama_pos_shift_attention_forward(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)

attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

if self.config.pretraining_tp > 1:
attn_output = attn_output.split(
self.hidden_size // self.config.pretraining_tp, dim=2
)
o_proj_slices = self.o_proj.weight.split(
self.hidden_size // self.config.pretraining_tp, dim=1
)
attn_output = sum(
[
F.linear(attn_output[i], o_proj_slices[i])
for i in range(self.config.pretraining_tp)
]
)
else:
attn_output = self.o_proj(attn_output)

if not output_attentions:
attn_weights = None

return attn_output, attn_weights, past_key_value
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value


def enable_llama_pos_shift_attention(model):
Expand Down
1 change: 0 additions & 1 deletion models/turbine_models/tests/stateless_llama_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,7 +193,6 @@ def test_streaming_vmfb_comparison(self):

# See: https://github.com/nod-ai/SHARK-Turbine/issues/560
# Developed issues related to the pytorch 2.3 upgrade.
@unittest.expectedFailure
def test_rerotated_torch_comparison(self):
torch_str = llm_runner.run_torch_llm(
"Trelis/Llama-2-7b-chat-hf-function-calling-v2",
Expand Down
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