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pre-compute

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This commit is contained in:
OlivierDehaene 2023-03-23 13:10:31 +01:00
parent cdc70f4c23
commit 19a04f22dd
1 changed files with 84 additions and 79 deletions
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27
server/text_generation_server/models/flash_neox_modeling.py
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@ -247,7 +247,7 @@ class FlashNeoxAttention(torch.nn.Module):
self.swap_dims = True self.swap_dims = True
def forward( def forward(
self, hidden_states, position_ids, cu_seqlens, max_s, layer_past, prefill self, hidden_states, position_ids, cu_seqlens, max_s, layer_past, layer_past_present_indices, cu_seqlens_q
): ):
if not self.swap_dims: if not self.swap_dims:
self._swap_dims() self._swap_dims()
@ -256,7 +256,7 @@ class FlashNeoxAttention(torch.nn.Module):
qkv = qkv.view(-1, 3, self.num_heads, self.head_size) qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
qkv_rot = self.rotary_emb(qkv, position_ids, max_s) qkv_rot = self.rotary_emb(qkv, position_ids, max_s)
if prefill: if layer_past_present_indices is None:
layer_past[...] = qkv_rot[:, 1:] layer_past[...] = qkv_rot[:, 1:]
attn_output = torch.empty_like(qkv[:, 0]) attn_output = torch.empty_like(qkv[:, 0])
@ -279,7 +279,7 @@ class FlashNeoxAttention(torch.nn.Module):
) )
else: else:
query = qkv_rot[:, 0] query = qkv_rot[:, 0]
layer_past[cu_seqlens[1:] - 1] = qkv_rot[:, 1:] layer_past[layer_past_present_indices] = qkv_rot[:, 1:]
attn_output = torch.empty_like(query) attn_output = torch.empty_like(query)
flash_attn_cuda.fwd( flash_attn_cuda.fwd(
@ -287,9 +287,9 @@ class FlashNeoxAttention(torch.nn.Module):
layer_past[:, 0], layer_past[:, 0],
layer_past[:, 1], layer_past[:, 1],
attn_output, attn_output,
torch.arange(len(cu_seqlens), dtype=torch.int32).to(query.device), cu_seqlens_q,
cu_seqlens, cu_seqlens,
torch.tensor(1, dtype=torch.int32).to(query.device), 1,
max_s, max_s,
0.0, 0.0,
self.softmax_scale, self.softmax_scale,
@ -376,7 +376,8 @@ class FlashNeoXLayer(nn.Module):
cu_seqlens, cu_seqlens,
max_s, max_s,
layer_past, layer_past,
prefill, layer_past_present_indices,
cu_seqlens_q,
): ):
if self.use_parallel_residual: if self.use_parallel_residual:
ln1_hidden_states, *rest = dropout_layer_norm.dropout_add_ln_fwd( ln1_hidden_states, *rest = dropout_layer_norm.dropout_add_ln_fwd(
@ -398,7 +399,7 @@ class FlashNeoXLayer(nn.Module):
) )
attn_output = self.attention( attn_output = self.attention(
ln1_hidden_states, position_ids, cu_seqlens, max_s, layer_past, prefill ln1_hidden_states, position_ids, cu_seqlens, max_s, layer_past, layer_past_present_indices, cu_seqlens_q
) )
ln2_hidden_states, *rest = dropout_layer_norm.dropout_add_ln_fwd( ln2_hidden_states, *rest = dropout_layer_norm.dropout_add_ln_fwd(
@ -441,7 +442,7 @@ class FlashNeoXLayer(nn.Module):
) )
hidden_states = self.attention( hidden_states = self.attention(
hidden_states, position_ids, cu_seqlens, max_s, layer_past, prefill hidden_states, position_ids, cu_seqlens, max_s, layer_past, layer_past_present_indices, cu_seqlens_q
) )
hidden_states, residual, *rest = dropout_layer_norm.dropout_add_ln_fwd( hidden_states, residual, *rest = dropout_layer_norm.dropout_add_ln_fwd(
@ -528,7 +529,6 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
): ):
hidden_states = self.embed_in(input_ids) hidden_states = self.embed_in(input_ids)
prefill = False
if past_key_values is None: if past_key_values is None:
past_key_values = hidden_states.new_empty( past_key_values = hidden_states.new_empty(
( (
@ -539,7 +539,11 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
self.head_size, self.head_size,
) )
) )
prefill = True layer_past_present_indices = None
cu_seqlens_q = None
else:
layer_past_present_indices = cu_seqlens[1:] - 1
cu_seqlens_q = torch.arange(len(cu_seqlens), dtype=torch.int32, device=hidden_states.device)
residual = None residual = None
for i, layer in enumerate(self.layers): for i, layer in enumerate(self.layers):
@ -550,7 +554,8 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
cu_seqlens, cu_seqlens,
max_s, max_s,
past_key_values[i], past_key_values[i],
prefill, layer_past_present_indices,
cu_seqlens_q
) )
hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.final_layer_norm(hidden_states)