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Fix llama attention optimization for XPU (#8855)

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* Fix llama attention optimization fo XPU

* fix chatglm2

* fix typo
This commit is contained in:
Yang Wang 2023-08-31 12:30:49 +08:00 committed by GitHub
parent 7b566bf686
commit 3b4f4e1c3d
2 changed files with 21 additions and 11 deletions
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18
python/llm/src/bigdl/llm/transformers/models/chatglm2.py
View file

@ -86,6 +86,7 @@ def chatglm2_attention_forward_8eb45c(
# ===================== # =====================
# Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
device = hidden_states.device
mixed_x_layer = self.query_key_value(hidden_states) mixed_x_layer = self.query_key_value(hidden_states)
if self.multi_query_attention: if self.multi_query_attention:
@ -151,11 +152,13 @@ def chatglm2_attention_forward_8eb45c(
self.kv_cache = (torch.empty(batch_size, self.kv_cache = (torch.empty(batch_size,
self.num_attention_heads_per_partition, self.num_attention_heads_per_partition,
self.max_cache_length, self.max_cache_length,
self.hidden_size_per_attention_head,), self.hidden_size_per_attention_head,
device=device),
torch.empty(batch_size, torch.empty(batch_size,
self.num_attention_heads_per_partition, self.num_attention_heads_per_partition,
self.max_cache_length, self.max_cache_length,
self.hidden_size_per_attention_head,)) self.hidden_size_per_attention_head,
device=device))
self.kv_cache[0][:, :, :past_length, :] = cache_k self.kv_cache[0][:, :, :past_length, :] = cache_k
self.kv_cache[1][:, :, :past_length, :] = cache_v self.kv_cache[1][:, :, :past_length, :] = cache_v
self.kv_cache[0][:, :, past_length:past_length + cur_length, :] = key_layer self.kv_cache[0][:, :, past_length:past_length + cur_length, :] = key_layer
@ -168,9 +171,11 @@ def chatglm2_attention_forward_8eb45c(
self.max_cache_length = max(KV_CACHE_ALLOC_MIN_LENGTH, cur_length) \ self.max_cache_length = max(KV_CACHE_ALLOC_MIN_LENGTH, cur_length) \
+ KV_CACHE_ALLOC_BLOCK_LENGTH + KV_CACHE_ALLOC_BLOCK_LENGTH
self.kv_cache = (torch.empty(batch_size, self.num_attention_heads_per_partition, self.kv_cache = (torch.empty(batch_size, self.num_attention_heads_per_partition,
self.max_cache_length, self.hidden_size_per_attention_head,), self.max_cache_length, self.hidden_size_per_attention_head,
device=device),
torch.empty(batch_size, self.num_attention_heads_per_partition, torch.empty(batch_size, self.num_attention_heads_per_partition,
self.max_cache_length, self.hidden_size_per_attention_head,)) self.max_cache_length, self.hidden_size_per_attention_head,
device=device))
self.kv_cache[0][:, :, :cur_length, :] = key_layer self.kv_cache[0][:, :, :cur_length, :] = key_layer
self.kv_cache[1][:, :, :cur_length, :] = value_layer self.kv_cache[1][:, :, :cur_length, :] = value_layer
@ -196,7 +201,7 @@ def chatglm2_attention_forward_8eb45c(
def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attention_mask): def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attention_mask):
pytorch_major_version = int(torch.__version__.split('.')[0]) pytorch_major_version = int(torch.__version__.split('.')[0])
if query_layer.size(0) > 1 and pytorch_major_version >= 2: if pytorch_major_version >= 2 and (query_layer.device.type == 'xpu' or query_layer.size(0) > 1):
query_layer = query_layer.permute(1, 2, 0, 3) query_layer = query_layer.permute(1, 2, 0, 3)
if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]: if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
@ -222,7 +227,6 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
is_causal=True) is_causal=True)
else: else:
if attention_mask is not None: if attention_mask is not None:
attention_mask = ~attention_mask
attention_mask = attention_mask.masked_fill(~attention_mask, -float('inf'), ) attention_mask = attention_mask.masked_fill(~attention_mask, -float('inf'), )
if torch.is_autocast_cpu_enabled(): if torch.is_autocast_cpu_enabled():
query_layer = query_layer.to(torch.get_autocast_cpu_dtype()) query_layer = query_layer.to(torch.get_autocast_cpu_dtype())
@ -258,6 +262,7 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
matmul_result = torch.empty( matmul_result = torch.empty(
output_size[0] * output_size[1], output_size[0] * output_size[1],
output_size[2], output_size[3], dtype=query_layer.dtype, output_size[2], output_size[3], dtype=query_layer.dtype,
device=query_layer.device
) )
# Raw attention scores. [b * np, sq, sk] # Raw attention scores. [b * np, sq, sk]
@ -313,6 +318,7 @@ def core_attn_forward_8eb45c(self, query_layer, key_layer, value_layer, attentio
context_layer = torch.empty( context_layer = torch.empty(
output_size[0] * output_size[1], output_size[0] * output_size[1],
output_size[2], value_layer.size(-1), dtype=value_layer.dtype, output_size[2], value_layer.size(-1), dtype=value_layer.dtype,
device=value_layer.device,
) )
torch.bmm(attention_probs, value_layer, out=context_layer) torch.bmm(attention_probs, value_layer, out=context_layer)
# change view [b, np, sq, hn] # change view [b, np, sq, hn]

12
python/llm/src/bigdl/llm/transformers/models/llama.py
View file

@ -129,11 +129,13 @@ def llama_attention_forward_4_31(
# value_states = torch.cat([past_key_value[1], value_states], dim=2) # value_states = torch.cat([past_key_value[1], value_states], dim=2)
if kv_seq_len > self.max_cache_length: if kv_seq_len > self.max_cache_length:
new_cache_key = torch.empty(bsz, self.num_heads, new_cache_key = torch.empty(bsz, self.num_heads,
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH, self.head_dim) kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH, self.head_dim,
device=device)
new_cache_key[:, :, :kv_seq_len-1, :] = self.kv_cache[0][:, :, :kv_seq_len-1, :] new_cache_key[:, :, :kv_seq_len-1, :] = self.kv_cache[0][:, :, :kv_seq_len-1, :]
new_cache_value = torch.empty(bsz, self.num_heads, new_cache_value = torch.empty(bsz, self.num_heads,
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH, self.head_dim) kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH, self.head_dim,
device=device)
new_cache_value[:, :, :kv_seq_len-1, :] = self.kv_cache[1][:, :, :kv_seq_len-1, :] new_cache_value[:, :, :kv_seq_len-1, :] = self.kv_cache[1][:, :, :kv_seq_len-1, :]
self.kv_cache = (new_cache_key, new_cache_value) self.kv_cache = (new_cache_key, new_cache_value)
self.max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH self.max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
@ -146,8 +148,10 @@ def llama_attention_forward_4_31(
# first token case # first token case
self.max_cache_length = max(min(self.max_position_embeddings, 2 * kv_seq_len), self.max_cache_length = max(min(self.max_position_embeddings, 2 * kv_seq_len),
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH) kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH)
self.kv_cache = (torch.empty(bsz, self.num_heads, self.max_cache_length, self.head_dim), self.kv_cache = (torch.empty(bsz, self.num_heads, self.max_cache_length, self.head_dim,
torch.empty(bsz, self.num_heads, self.max_cache_length, self.head_dim)) dtype=key_states.dtype, device=device),
torch.empty(bsz, self.num_heads, self.max_cache_length, self.head_dim,
dtype=key_states.dtype, device=device))
self.kv_cache[0][:, :, :kv_seq_len, :] = key_states self.kv_cache[0][:, :, :kv_seq_len, :] = key_states
self.kv_cache[1][:, :, :kv_seq_len, :] = value_states self.kv_cache[1][:, :, :kv_seq_len, :] = value_states