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Fix tests and naming

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Signed-off-by: char-1ee <xingjianli59@gmail.com>
This commit is contained in:
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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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()