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[infer]Fix bugs in llama and bloom #4588

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Merged
tiandiao123 merged 1 commit into from
Sep 1, 2023
Merged

[infer]Fix bugs in llama and bloom #4588

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24 changes: 9 additions & 15 deletions colossalai/inference/tensor_parallel/modeling/bloom.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,7 @@ def bloom_model_forward(
# if self.cache_manager.past_key_values_length > 0:
if infer_state.cache_manager.past_key_values_length > 0:
# update the past key values length in cache manager,
# TODO use BatchInferState.past_key_values_length instead the one in cache manager
# NOTE use BatchInferState.past_key_values_length instead the one in cache manager
past_key_values_length = infer_state.cache_manager.past_key_values_length
seq_length_with_past = seq_length_with_past + past_key_values_length

Expand Down Expand Up @@ -178,7 +178,7 @@ def bloom_model_forward(
else:
attention_mask = attention_mask.to(hidden_states.device)

# TODO revise: we might want to store a single 1D alibi(length is #heads) in model,
# NOTE revise: we might want to store a single 1D alibi(length is #heads) in model,
# or store to BatchInferState to prevent re-calculating
# When we have multiple process group (e.g. dp together with tp), we need to pass the pg to here
# alibi = generate_alibi(self.num_heads).contiguous().cuda()
Expand Down Expand Up @@ -445,6 +445,9 @@ def bloom_attention_forward(
mem_manager = infer_state.cache_manager
layer_id = infer_state.decode_layer_id

if layer_id == 0: # once per model.forward
infer_state.cache_manager.past_key_values_length += q_length # += 1

if infer_state.is_context_stage:
# context process
max_input_len = q_length
Expand All @@ -461,10 +464,6 @@ def bloom_attention_forward(
bloom_context_attn_fwd(q, k, v, output, b_start_loc, b_seq_len, max_input_len, alibi)

context_layer = output.view(batch_size, q_length, H * D_HEAD)
# record the length of past key values cache when entering the first attention layer in bloom block,
# since we won't return past_key_value_cache right now
if layer_id == 0: # once per model.forward
infer_state.cache_manager.past_key_values_length = q_length # seq_len
else:
# query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
# need shape: batch_size, H, D_HEAD (q_length == 1), input q shape : (batch_size, q_length(1), H, D_HEAD)
Expand All @@ -485,20 +484,15 @@ def bloom_attention_forward(
copy_kv_cache_to_dest(k, infer_state.decode_mem_index, mem_manager.key_buffer[layer_id])
copy_kv_cache_to_dest(v, infer_state.decode_mem_index, mem_manager.value_buffer[layer_id])

b_start_loc = infer_state.start_loc[:batch_size]
b_loc = infer_state.block_loc[:batch_size, :]
b_seq_len = infer_state.seq_len[:batch_size]
max_len_in_batch = mem_manager.past_key_values_length + q_length
b_start_loc = infer_state.start_loc
b_loc = infer_state.block_loc
b_seq_len = infer_state.seq_len
output = torch.empty_like(q)
token_attention_fwd(q, mem_manager.key_buffer[layer_id], mem_manager.value_buffer[layer_id], output, b_loc,
b_start_loc, b_seq_len, max_len_in_batch, alibi)
b_start_loc, b_seq_len, infer_state.cache_manager.past_key_values_length, alibi)

context_layer = output.view(batch_size, q_length, H * D_HEAD)

if layer_id == 0: # once per model.forward
assert infer_state.cache_manager.past_key_values_length != 0
infer_state.cache_manager.past_key_values_length += q_length # += 1

# update layer id
infer_state.decode_layer_id += 1

Expand Down
80 changes: 45 additions & 35 deletions colossalai/inference/tensor_parallel/modeling/llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,12 +54,16 @@ def llama_model_forward(

infer_state = self.infer_state
b_seq_len_numpy = infer_state.seq_len.cpu().numpy()
position_ids = torch.from_numpy(
np.concatenate([np.arange(0, b_seq_len_numpy[i]) for i in range(len(b_seq_len_numpy))], axis=0)).cuda()

# this equals
infer_state.position_cos = torch.index_select(self._cos_cached, 0, position_ids).view(position_ids.shape[0], -1)
infer_state.position_sin = torch.index_select(self._sin_cached, 0, position_ids).view(position_ids.shape[0], -1)
if HAS_VLLM_KERNERL:
position_ids = torch.from_numpy(
np.concatenate([np.arange(0, b_seq_len_numpy[i]) for i in range(len(b_seq_len_numpy))], axis=0)).cuda()

# this equals
infer_state.position_cos = torch.index_select(self._cos_cached, 0,
position_ids).view(position_ids.shape[0], -1)
infer_state.position_sin = torch.index_select(self._sin_cached, 0,
position_ids).view(position_ids.shape[0], -1)

return_dict = return_dict if return_dict is not None else self.config.use_return_dict

Expand Down Expand Up @@ -241,64 +245,70 @@ def llama_flash_attn_kvcache_forward(

bsz, q_len, _ = hidden_states.size()

# TODO might think about better way to handle transposed k and v
# NOTE might think about better way to handle transposed k and v
# key_states [bs, seq_len, num_heads, head_dim/embed_size_per_head]
# key_states_transposed [bs, num_heads, seq_len, head_dim/embed_size_per_head]

query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
key_states_transposed = key_states.transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)
key_states_transposed = key_states.transpose(1, 2)

# cos, sin = self.rotary_emb(value_states_transposed, seq_len=kv_seq_len)
cos, sin = infer_state.position_cos, infer_state.position_sin
# NOTE might want to revise
# need some way to record the length of past key values cache
# since we won't return past_key_value_cache right now
if infer_state.decode_layer_id == 0: # once per model.forward
infer_state.cache_manager.past_key_values_length += q_len # seq_len

if HAS_VLLM_KERNERL:
cos, sin = infer_state.position_cos, infer_state.position_sin
cos_sin_cache = torch.cat((cos, sin), dim=-1)
rotary_embedding_neox(position_ids, query_states, key_states_transposed, self.head_dim, cos_sin_cache)
key_states = key_states_transposed.transpose(1, 2)
else:
# NOTE: there are some issues for original rotary_embedding_neox of huggingface
value_states_transposed = value_states.transpose(1, 2)
cos, sin = self.rotary_emb(value_states_transposed,
seq_len=infer_state.cache_manager.past_key_values_length)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states_transposed, cos, sin, position_ids)
key_states = key_states_transposed.transpose(1, 2)

def _copy_kv_to_mem_cache(layer_id, key_buffer, value_buffer, context_mem_index, mem_manager):
num_heads = key_buffer.shape[2]
head_dim = key_buffer.shape[3]
key_buffer = key_buffer.view(-1, num_heads, head_dim)
value_buffer = value_buffer.view(-1, num_heads, head_dim)
copy_kv_cache_to_dest(key_buffer, context_mem_index, mem_manager.key_buffer[layer_id])
copy_kv_cache_to_dest(value_buffer, context_mem_index, mem_manager.value_buffer[layer_id])
return

# copy key and value calculated in current step to memory manager
if infer_state.is_context_stage:
_copy_kv_to_mem_cache(infer_state.decode_layer_id, key_states, value_states, infer_state.context_mem_index,
infer_state.cache_manager)
else:
_copy_kv_to_mem_cache(infer_state.decode_layer_id, key_states, value_states, infer_state.decode_mem_index,
infer_state.cache_manager)

# FIXME might want to revise
# need some way to record the length of past key values cache
# since we won't return past_key_value_cache right now
if infer_state.decode_layer_id == 0: # once per model.forward
infer_state.cache_manager.past_key_values_length += q_len # seq_len

query_states = query_states.transpose(1, 2)
key_states = key_states.reshape(-1, self.num_heads, self.head_dim)
value_states = value_states.reshape(-1, self.num_heads, self.head_dim)
query_states = query_states.transpose(1, 2).reshape(-1, self.num_heads, self.head_dim)

if infer_state.is_context_stage:
# first token generation

attn_output = torch.empty_like(query_states)
# copy key and value calculated in current step to memory manager
_copy_kv_to_mem_cache(infer_state.decode_layer_id, key_states, value_states, infer_state.context_mem_index,
infer_state.cache_manager)

# calcu_shape for context_attention_fwd
calcu_shape1 = (-1, self.num_heads, self.head_dim)
attn_output = torch.empty_like(query_states)

llama_context_attn_fwd(query_states.view(calcu_shape1), key_states.view(calcu_shape1),
value_states.view(calcu_shape1), attn_output.view(calcu_shape1),
infer_state.start_loc, infer_state.seq_len,
infer_state.cache_manager.past_key_values_length)
llama_context_attn_fwd(query_states, key_states, value_states, attn_output, infer_state.start_loc,
infer_state.seq_len, infer_state.cache_manager.past_key_values_length)
else:

if infer_state.decode_is_contiguous:
# if decode is contiguous, then we copy to key cache and value cache in cache manager directly
cache_k = infer_state.cache_manager.key_buffer[infer_state.decode_layer_id][
infer_state.decode_mem_start:infer_state.decode_mem_end, :, :]
cache_v = infer_state.cache_manager.value_buffer[infer_state.decode_layer_id][
infer_state.decode_mem_start:infer_state.decode_mem_end, :, :]
cache_k.copy_(key_states)
cache_v.copy_(value_states)
else:
# if decode is not contiguous, use triton kernel to copy key and value cache
# k, v shape: [batch_size, num_heads, head_dim/embed_size_per_head
_copy_kv_to_mem_cache(infer_state.decode_layer_id, key_states, value_states,
infer_state.decode_mem_index, infer_state.cache_manager)

# second token and follows
# kv = torch.stack((key_states, value_states), dim=2)
# (batch_size, seqlen, nheads, headdim)
Expand Down