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Fix tests and naming 

Signed-off-by: char-1ee <xingjianli59@gmail.com>
pull/5771/head
char-1ee 2024-06-03 05:41:32 +00:00
parent 04386d9eff
commit eec77e5702
5 changed files with 154 additions and 250 deletions
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8
colossalai/inference/core/engine.py
View File

@ -18,7 +18,7 @@ from transformers.models.llama.modeling_llama import LlamaForCausalLM
from colossalai.accelerator import get_accelerator
from colossalai.cluster import ProcessGroupMesh
from colossalai.inference.batch_bucket import BatchBucket
from colossalai.inference.config import InferenceConfig, InputMetaData, ModelInferenceConfig
from colossalai.inference.config import InferenceConfig, InputMetaData
from colossalai.inference.graph_runner import CUDAGraphRunner
from colossalai.inference.modeling.policy import model_policy_map
from colossalai.inference.sampler import search_tokens
@ -72,9 +72,8 @@ class InferenceEngine:
self.verbose = verbose
self.logger = get_dist_logger(__name__)
self.model_inference_config = inference_config.to_model_inference_config()
self.init_model(model_or_path, model_policy, self.model_inference_config)
self.init_model(model_or_path, model_policy)
self.generation_config = inference_config.to_generation_config(self.model_config)
self.generation_config_dict = self.generation_config.to_dict()
@ -113,7 +112,6 @@ class InferenceEngine:
self,
model_or_path: Union[nn.Module, str],
model_policy: Union[Policy, Type[Policy]] = None,
model_inference_config: ModelInferenceConfig = None,
):
"""
Shard model or/and Load weight
@ -178,7 +176,6 @@ class InferenceEngine:
self.model = self._shardformer(
model,
model_policy,
model_inference_config,
None,
tp_group=tp_group,
)
@ -299,7 +296,6 @@ class InferenceEngine:
self,
model: nn.Module,
model_policy: Policy,
model_inference_config: ModelInferenceConfig,
stage_manager: PipelineStageManager = None,
tp_group: ProcessGroupMesh = None,
) -> nn.Module:

98
colossalai/inference/modeling/backends/attention_backend.py
View File

@ -8,15 +8,16 @@ from colossalai.inference.utils import can_use_flash_attn2
from colossalai.logging import get_dist_logger
from colossalai.kernel.kernel_loader import InferenceOpsLoader
from colossalai.kernel.triton import (
context_attention_unpadded,
context_attention_unpadded,
flash_decoding_attention,
)
logger = get_dist_logger(__name__)
inference_ops = InferenceOpsLoader().load()
@dataclass
class AttentionMetaData:
class AttentionMetaData:
query_states: torch.Tensor
key_states: torch.Tensor
value_states: torch.Tensor
@ -32,7 +33,8 @@ class AttentionMetaData:
output_tensor: torch.Tensor = None
use_spec_dec: bool = False
use_alibi_attn: bool = False
use_cuda_kernel: bool = False
class AttentionBackend(ABC):
@abstractmethod
@ -42,46 +44,30 @@ class AttentionBackend(ABC):
@abstractmethod
def decode(self, attn_metadatas: AttentionMetaData, **kwargs):
raise NotImplementedError
class CudaAttentionBackend(AttentionBackend):
def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
if not attn_metadata.use_spec_dec:
token_nums = kwargs.get('token_nums', -1)
attn_output = flash_attn_varlen_func(
attn_metadata.query_states,
attn_metadata.key_states,
attn_metadata.value_states,
cu_seqlens_q=attn_metadata.cu_seqlens,
cu_seqlens_k=attn_metadata.cu_seqlens,
max_seqlen_k=attn_metadata.kv_seq_len,
max_seqlen_v=attn_metadata.kv_seq_len,
dropout_p=0.0,
softmax_scale=attn_metadata.sm_scale,
causal=True,
)
attn_output = attn_output.view(token_nums, -1)
else:
attn_output = context_attention_unpadded(
q=attn_metadata.query_states,
k=attn_metadata.key_states,
v=attn_metadata.value_states,
k_cache=attn_metadata.k_cache,
v_cache=attn_metadata.v_cache,
context_lengths=attn_metadata.sequence_lengths,
block_tables=attn_metadata.block_tables,
block_size=attn_metadata.block_size,
output=attn_metadata.output_tensor,
max_seq_len=attn_metadata.kv_seq_len,
sm_scale=attn_metadata.sm_scale,
use_new_kcache_layout=True,
)
token_nums = kwargs.get("token_nums", -1)
attn_output = flash_attn_varlen_func(
attn_metadata.query_states,
attn_metadata.key_states,
attn_metadata.value_states,
cu_seqlens_q=attn_metadata.cu_seqlens,
cu_seqlens_k=attn_metadata.cu_seqlens,
max_seqlen_q=attn_metadata.kv_seq_len,
max_seqlen_k=attn_metadata.kv_seq_len,
dropout_p=0.0,
softmax_scale=attn_metadata.sm_scale,
causal=True,
alibi_slopes=attn_metadata.alibi_slopes,
)
attn_output = attn_output.view(token_nums, -1)
return attn_output
def decode(self, attn_metadata: AttentionMetaData, **kwargs):
fd_inter_tensor = kwargs.get('fd_inter_tensor', None)
fd_inter_tensor = kwargs.get("fd_inter_tensor", None)
output_tensor = attn_metadata.output_tensor
inference_ops.flash_decoding_attention(
output_tensor,
@ -99,8 +85,8 @@ class CudaAttentionBackend(AttentionBackend):
attn_metadata.sm_scale,
)
return output_tensor
class TritonAttentionBackend(AttentionBackend):
def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
return context_attention_unpadded(
@ -113,13 +99,14 @@ class TritonAttentionBackend(AttentionBackend):
block_tables=attn_metadata.block_tables,
block_size=attn_metadata.block_size,
output=attn_metadata.output_tensor,
alibi_slopes=attn_metadata.alibi_slopes,
max_seq_len=attn_metadata.kv_seq_len,
sm_scale=attn_metadata.sm_scale,
use_new_kcache_layout=False,
use_new_kcache_layout=attn_metadata.use_cuda_kernel,
)
def decode(self, attn_metadata: AttentionMetaData, **kwargs):
fd_inter_tensor = kwargs.get('fd_inter_tensor', None)
fd_inter_tensor = kwargs.get("fd_inter_tensor", None)
return flash_decoding_attention(
q=attn_metadata.query_states,
k_cache=attn_metadata.k_cache,
@ -131,16 +118,25 @@ class TritonAttentionBackend(AttentionBackend):
output=attn_metadata.output_tensor,
mid_output=fd_inter_tensor.mid_output,
mid_output_lse=fd_inter_tensor.mid_output_lse,
alibi_slopes=attn_metadata.alibi_slopes,
sm_scale=attn_metadata.sm_scale,
kv_group_num=kwargs.get('num_key_value_groups', 0),
q_len=kwargs.get('q_len', 1),
kv_group_num=kwargs.get("num_key_value_groups", 1),
q_len=kwargs.get("q_len", 1),
)
def get_attention_backend(use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype) -> AttentionBackend:
use_flash_attn = can_use_flash_attn2(dtype)
def get_attention_backend(
use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype
) -> AttentionBackend:
"""
Get the attention backend based on the inference configurations. Only when:
1. using CUDA kernel (use_cuda_kernel=True)
2. can use flash attention (flash-attn installed and dtype is fp16 or bf16)
3. not using speculative decoding (currently cuda kernel not support speculative decoding)
will the CUDA-kernel-based backend be used for attention layer computations. Otherwise, use Triton attention backend.
"""
use_flash_attn = can_use_flash_attn2(dtype)
if use_cuda_kernel and use_flash_attn and not use_spec_dec:
return CudaAttentionBackend()
else:
return TritonAttentionBackend()

103
colossalai/inference/modeling/backends/attention_context.py → colossalai/inference/modeling/backends/pre_attention_backend.py
View File

@ -13,60 +13,52 @@ from colossalai.kernel.triton import (
logger = get_dist_logger(__name__)
inference_ops = InferenceOpsLoader().load()
class AttentionContext(ABC):
class PreAttentionBackend(ABC):
@abstractmethod
def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
raise NotImplementedError
@abstractmethod
def decode(self, attn_metadata: AttentionMetaData, **kwargs):
raise NotImplementedError
class CudaAttentionContext(AttentionContext):
class CudaPreAttentionBackend(PreAttentionBackend):
def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
if not attn_metadata.use_spec_dec:
if not attn_metadata.use_alibi_attn:
inference_ops.rotary_embedding(
attn_metadata.query_states,
attn_metadata.key_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
kwargs.get('high_precision', False),
)
inference_ops.context_kv_cache_memcpy(
attn_metadata.key_states,
attn_metadata.value_states,
attn_metadata.k_cache,
attn_metadata.v_cache,
attn_metadata.sequence_lengths,
attn_metadata.cu_seqlens,
attn_metadata.block_tables,
attn_metadata.kv_seq_len,
)
else:
rotary_embedding(
if not attn_metadata.use_alibi_attn:
inference_ops.rotary_embedding(
attn_metadata.query_states,
attn_metadata.key_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
)
kwargs.get("cos", None),
kwargs.get("sin", None),
kwargs.get("high_precision", False),
)
inference_ops.context_kv_cache_memcpy(
attn_metadata.key_states,
attn_metadata.value_states,
attn_metadata.k_cache,
attn_metadata.v_cache,
attn_metadata.sequence_lengths,
attn_metadata.cu_seqlens,
attn_metadata.block_tables,
attn_metadata.kv_seq_len,
)
def decode(self, attn_metadata: AttentionMetaData, **kwargs):
if attn_metadata.use_alibi_attn:
if not attn_metadata.use_alibi_attn:
inference_ops.rotary_embedding_and_cache_copy(
attn_metadata.query_states,
attn_metadata.key_states,
attn_metadata.value_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
kwargs.get("cos", None),
kwargs.get("sin", None),
attn_metadata.k_cache,
attn_metadata.v_cache,
attn_metadata.sequence_lengths,
attn_metadata.block_tables,
attn_metadata.high_precision,
kwargs.get("high_precision", None),
)
else:
inference_ops.decode_kv_cache_memcpy(
@ -77,58 +69,63 @@ class CudaAttentionContext(AttentionContext):
attn_metadata.sequence_lengths,
attn_metadata.block_tables,
)
class TritonAttentionContext(AttentionContext):
class TritonPreAttentionBackend(PreAttentionBackend):
def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
if not attn_metadata.use_alibi_attn:
rotary_embedding(
attn_metadata.query_states,
attn_metadata.key_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
kwargs.get("cos", None),
kwargs.get("sin", None),
)
def decode(self, attn_metadata: AttentionMetaData, **kwargs):
if not attn_metadata.use_spec_dec and not attn_metadata.use_alibi_attn:
decoding_fused_rotary_embedding(
attn_metadata.query_states,
attn_metadata.key_states,
attn_metadata.value_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
kwargs.get("cos", None),
kwargs.get("sin", None),
attn_metadata.k_cache,
attn_metadata.v_cache,
attn_metadata.block_tables,
attn_metadata.sequence_lengths,
)
else:
else: # else if using speculative decoding
if not attn_metadata.use_alibi_attn:
rotary_embedding(
attn_metadata.query_states,
attn_metadata.key_states,
kwargs.get('cos', None),
kwargs.get('sin', None),
kwargs.get("cos", None),
kwargs.get("sin", None),
)
copy_k_to_blocked_cache(
attn_metadata.key_states,
attn_metadata.k_cache,
kv_lengths=attn_metadata.sequence_lengths,
block_tables=attn_metadata.block_tables,
n=kwargs.get('q_len', 1)
n=kwargs.get("q_len", 1),
)
copy_k_to_blocked_cache(
attn_metadata.value_states,
attn_metadata.v_cache,
kv_lengths=attn_metadata.sequence_lengths,
block_tables=attn_metadata.block_tables,
n=kwargs.get('q_len', 1)
n=kwargs.get("q_len", 1),
)
def get_attention_context(use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype) -> AttentionContext:
def get_pre_attention_backend(
use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype
) -> PreAttentionBackend:
"""
Get the backend for pre-attention computations, including potisional encoding like RoPE and KV cache initialization.
"""
use_flash_attn = can_use_flash_attn2(dtype)
if use_cuda_kernel and use_flash_attn and not use_spec_dec:
return CudaAttentionContext()
return CudaPreAttentionBackend()
else:
return TritonAttentionContext()
return TritonPreAttentionBackend()

184
colossalai/inference/modeling/models/nopadding_baichuan.py
View File

@ -10,6 +10,8 @@ from torch.distributed import ProcessGroup
from colossalai.inference.flash_decoding_utils import FDIntermTensors
from colossalai.inference.utils import get_alibi_slopes
from colossalai.inference.modeling.models.nopadding_llama import NopadLlamaMLP
from colossalai.inference.modeling.backends.attention_backend import AttentionMetaData, get_attention_backend
from colossalai.inference.modeling.backends.pre_attention_backend import get_pre_attention_backend
from colossalai.kernel.kernel_loader import InferenceOpsLoader
from colossalai.kernel.triton import (
context_attention_unpadded,
@ -23,28 +25,8 @@ from colossalai.logging import get_dist_logger
from colossalai.shardformer.layer.parallel_module import ParallelModule
from colossalai.tensor.d_tensor import Layout, distribute_tensor, is_distributed_tensor
logger = get_dist_logger(__name__)
try:
from flash_attn import flash_attn_varlen_func
use_flash_attn2 = True
except ImportError:
use_flash_attn2 = False
logger.warning(f"flash_attn2 has not been installed yet, we will use triton flash attn instead.")
logger = get_dist_logger(__name__)
try:
from flash_attn import flash_attn_varlen_func
use_flash_attn2 = True
except ImportError:
use_flash_attn2 = False
logger.warning(f"flash_attn2 has not been installed yet, we will use triton flash attn instead.")
inference_ops = InferenceOpsLoader().load()
logger = get_dist_logger(__name__)
@ -251,122 +233,54 @@ class NopadBaichuanAttention(ParallelModule):
)
block_size = k_cache.size(-2)
if is_prompts:
if not is_verifier and use_cuda_kernel and query_states.dtype != torch.float32 and use_flash_attn2:
# flash attn 2 currently only supports FP16/BF16.
if not self.use_alibi_attn:
inference_ops.rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1], high_precision)
inference_ops.context_kv_cache_memcpy(
key_states, value_states, k_cache, v_cache, sequence_lengths, cu_seqlens, block_tables, kv_seq_len
)
attn_output = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=kv_seq_len,
max_seqlen_k=kv_seq_len,
dropout_p=0.0,
softmax_scale=sm_scale,
causal=True,
alibi_slopes=self.alibi_slopes,
)
attn_output = attn_output.view(token_nums, -1)
else:
if not self.use_alibi_attn:
rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
attn_output = context_attention_unpadded(
q=query_states,
k=key_states,
v=value_states,
k_cache=k_cache,
v_cache=v_cache,
context_lengths=sequence_lengths,
block_tables=block_tables,
block_size=block_size,
output=output_tensor,
alibi_slopes=self.alibi_slopes,
max_seq_len=kv_seq_len,
sm_scale=sm_scale,
use_new_kcache_layout=use_cuda_kernel,
)
else:
attn_metadata = AttentionMetaData(
query_states=query_states,
key_states=key_states,
value_states=value_states,
k_cache=k_cache,
v_cache=v_cache,
block_tables=block_tables,
block_size=block_size,
kv_seq_len=kv_seq_len,
sequence_lengths=sequence_lengths,
sm_scale=sm_scale,
alibi_slopes=self.alibi_slopes,
cu_seqlens=cu_seqlens,
output_tensor=output_tensor,
use_spec_dec=is_verifier,
use_alibi_attn=self.use_alibi_attn,
use_cuda_kernel=use_cuda_kernel,
)
attention_backend = get_attention_backend(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
pre_attention_backend = get_pre_attention_backend(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
if is_prompts: # prefilling stage
pre_attention_backend.prefill(
attn_metadata,
cos=cos_sin[0],
sin=cos_sin[1],
high_precision=high_precision,
)
attn_output = attention_backend.prefill(
attn_metadata,
token_nums=token_nums,
)
else: # decoding stage
q_len = tokens_to_verify + 1 if is_verifier else 1
if use_cuda_kernel:
if not self.use_alibi_attn:
inference_ops.rotary_embedding_and_cache_copy(
query_states,
key_states,
value_states,
cos_sin[0],
cos_sin[1],
k_cache,
v_cache,
sequence_lengths,
block_tables,
high_precision,
)
else:
inference_ops.decode_kv_cache_memcpy(
key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables
)
inference_ops.flash_decoding_attention(
output_tensor,
query_states,
k_cache,
v_cache,
sequence_lengths,
block_tables,
block_size,
kv_seq_len,
fd_inter_tensor.mid_output,
fd_inter_tensor.exp_sums,
fd_inter_tensor.max_logits,
self.alibi_slopes,
sm_scale,
)
attn_output = output_tensor
else:
if not is_verifier and not self.use_alibi_attn:
decoding_fused_rotary_embedding(
query_states,
key_states,
value_states,
cos_sin[0],
cos_sin[1],
k_cache,
v_cache,
block_tables,
sequence_lengths,
)
else:
if not self.use_alibi_attn:
rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
copy_k_to_blocked_cache(
key_states, k_cache, kv_lengths=sequence_lengths, block_tables=block_tables, n=q_len
)
copy_k_to_blocked_cache(
value_states, v_cache, kv_lengths=sequence_lengths, block_tables=block_tables, n=q_len
)
attn_output = flash_decoding_attention(
q=query_states,
k_cache=k_cache,
v_cache=v_cache,
kv_seq_len=sequence_lengths,
block_tables=block_tables,
block_size=block_size,
max_seq_len_in_batch=kv_seq_len,
output=output_tensor,
mid_output=fd_inter_tensor.mid_output,
mid_output_lse=fd_inter_tensor.mid_output_lse,
alibi_slopes=self.alibi_slopes,
sm_scale=sm_scale,
q_len=q_len,
)
pre_attention_backend.decode(
attn_metadata,
cos=cos_sin[0],
sin=cos_sin[1],
q_len=q_len,
)
attn_output = attention_backend.decode(
attn_metadata,
fd_inter_tensor=fd_inter_tensor,
q_len=q_len,
)
attn_output = attn_output.view(-1, self.hidden_size)
attn_output = self.o_proj(attn_output)

11
colossalai/inference/modeling/models/nopadding_llama.py
View File

@ -19,7 +19,7 @@ from transformers.models.llama.modeling_llama import (
from colossalai.inference.config import InputMetaData
from colossalai.inference.flash_decoding_utils import FDIntermTensors
from colossalai.inference.modeling.backends.attention_backend import get_attention_backend, AttentionMetaData
from colossalai.inference.modeling.backends.attention_context import get_attention_context
from colossalai.inference.modeling.backends.pre_attention_backend import get_pre_attention_backend
from colossalai.inference.utils import can_use_flash_attn2
from colossalai.kernel.kernel_loader import InferenceOpsLoader
from colossalai.kernel.triton import (
@ -121,7 +121,7 @@ def llama_model_forward(
cos_sin = (self._cos_cached[rotary_indexes], self._sin_cached[rotary_indexes])
elif use_cuda_kernel:
if inputmetadata.dtype != torch.float32 and can_use_flash_attn2():
if can_use_flash_attn2(inputmetadata.dtype):
cu_seqlens = F.pad(torch.cumsum(sequence_lengths, dim=0, dtype=torch.torch.int32), (1, 0))
hidden_dim = self._cos_cached.size(-1)
@ -544,13 +544,14 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
output_tensor=output_tensor,
use_spec_dec=is_verifier,
use_alibi_attn=False,
use_cuda_kernel=use_cuda_kernel,
)
attention_backend = get_attention_backend(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
attention_context = get_attention_context(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
pre_attention_backend = get_pre_attention_backend(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
if is_prompts: # prefilling stage
attention_context.prefill(
pre_attention_backend.prefill(
attn_metadata,
cos=cos_sin[0],
sin=cos_sin[1],
@ -563,7 +564,7 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
else: # decoding stage
q_len = tokens_to_verify + 1 if is_verifier else 1
attention_context.decode(
pre_attention_backend.decode(
attn_metadata,
cos=cos_sin[0],
sin=cos_sin[1],