mirror of
https://github.com/hpcaitech/ColossalAI.git
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Refactor modeling by adding attention backend
Signed-off-by: char-1ee <xingjianli59@gmail.com>
This commit is contained in:
char-1ee
0
colossalai/inference/modeling/backends/__init__.py
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0
colossalai/inference/modeling/backends/__init__.py
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146
colossalai/inference/modeling/backends/attention_backend.py
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146
colossalai/inference/modeling/backends/attention_backend.py
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@@ -0,0 +1,146 @@
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from flash_attn import flash_attn_varlen_func
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import torch
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from colossalai.inference.config import InputMetaData
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from colossalai.inference.utils import can_use_flash_attn2
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from colossalai.logging import get_dist_logger
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from colossalai.kernel.kernel_loader import InferenceOpsLoader
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from colossalai.kernel.triton import (
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context_attention_unpadded,
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flash_decoding_attention,
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)
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logger = get_dist_logger(__name__)
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inference_ops = InferenceOpsLoader().load()
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@dataclass
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class AttentionMetaData:
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query_states: torch.Tensor
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key_states: torch.Tensor
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value_states: torch.Tensor
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k_cache: torch.Tensor
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v_cache: torch.Tensor
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block_tables: torch.Tensor
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block_size: int
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kv_seq_len: int = None
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sequence_lengths: torch.Tensor = None
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cu_seqlens: torch.Tensor = None
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sm_scale: int = None
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alibi_slopes: torch.Tensor = None
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output_tensor: torch.Tensor = None
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use_spec_dec: bool = False
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use_alibi_attn: bool = False
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class AttentionBackend(ABC):
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@abstractmethod
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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raise NotImplementedError
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@abstractmethod
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def decode(self, attn_metadatas: AttentionMetaData, **kwargs):
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raise NotImplementedError
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class CudaAttentionBackend(AttentionBackend):
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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if not attn_metadata.use_spec_dec:
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token_nums = kwargs.get('token_nums', -1)
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attn_output = flash_attn_varlen_func(
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attn_metadata.query_states,
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attn_metadata.key_states,
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attn_metadata.value_states,
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cu_seqlens_q=attn_metadata.cu_seqlens,
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cu_seqlens_k=attn_metadata.cu_seqlens,
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max_seqlen_k=attn_metadata.kv_seq_len,
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max_seqlen_v=attn_metadata.kv_seq_len,
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dropout_p=0.0,
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softmax_scale=attn_metadata.sm_scale,
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causal=True,
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)
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attn_output = attn_output.view(token_nums, -1)
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else:
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attn_output = context_attention_unpadded(
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q=attn_metadata.query_states,
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k=attn_metadata.key_states,
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v=attn_metadata.value_states,
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k_cache=attn_metadata.k_cache,
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v_cache=attn_metadata.v_cache,
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context_lengths=attn_metadata.sequence_lengths,
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block_tables=attn_metadata.block_tables,
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block_size=attn_metadata.block_size,
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output=attn_metadata.output_tensor,
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max_seq_len=attn_metadata.kv_seq_len,
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sm_scale=attn_metadata.sm_scale,
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use_new_kcache_layout=True,
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)
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return attn_output
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def decode(self, attn_metadata: AttentionMetaData, **kwargs):
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fd_inter_tensor = kwargs.get('fd_inter_tensor', None)
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output_tensor = attn_metadata.output_tensor
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inference_ops.flash_decoding_attention(
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output_tensor,
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attn_metadata.query_states,
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attn_metadata.k_cache,
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attn_metadata.v_cache,
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attn_metadata.sequence_lengths,
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attn_metadata.block_tables,
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attn_metadata.block_size,
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attn_metadata.kv_seq_len,
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fd_inter_tensor.mid_output,
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fd_inter_tensor.exp_sums,
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fd_inter_tensor.max_logits,
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attn_metadata.alibi_slopes,
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attn_metadata.sm_scale,
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)
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return output_tensor
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class TritonAttentionBackend(AttentionBackend):
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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return context_attention_unpadded(
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q=attn_metadata.query_states,
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k=attn_metadata.key_states,
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v=attn_metadata.value_states,
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k_cache=attn_metadata.k_cache,
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v_cache=attn_metadata.v_cache,
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context_lengths=attn_metadata.sequence_lengths,
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block_tables=attn_metadata.block_tables,
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block_size=attn_metadata.block_size,
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output=attn_metadata.output_tensor,
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max_seq_len=attn_metadata.kv_seq_len,
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sm_scale=attn_metadata.sm_scale,
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use_new_kcache_layout=False,
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)
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def decode(self, attn_metadata: AttentionMetaData, **kwargs):
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fd_inter_tensor = kwargs.get('fd_inter_tensor', None)
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return flash_decoding_attention(
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q=attn_metadata.query_states,
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k_cache=attn_metadata.k_cache,
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v_cache=attn_metadata.v_cache,
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kv_seq_len=attn_metadata.sequence_lengths,
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block_tables=attn_metadata.block_tables,
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block_size=attn_metadata.block_size,
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max_seq_len_in_batch=attn_metadata.kv_seq_len,
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output=attn_metadata.output_tensor,
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mid_output=fd_inter_tensor.mid_output,
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mid_output_lse=fd_inter_tensor.mid_output_lse,
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sm_scale=attn_metadata.sm_scale,
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kv_group_num=kwargs.get('num_key_value_groups', 0),
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q_len=kwargs.get('q_len', 1),
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)
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def get_attention_backend(use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype) -> AttentionBackend:
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use_flash_attn = can_use_flash_attn2(dtype)
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if use_cuda_kernel and use_flash_attn and not use_spec_dec:
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return CudaAttentionBackend()
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else:
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return TritonAttentionBackend()
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134
colossalai/inference/modeling/backends/attention_context.py
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134
colossalai/inference/modeling/backends/attention_context.py
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@@ -0,0 +1,134 @@
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from abc import ABC, abstractmethod
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import torch
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from colossalai.inference.utils import can_use_flash_attn2
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from colossalai.kernel.kernel_loader import InferenceOpsLoader
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from colossalai.inference.modeling.backends.attention_backend import AttentionMetaData
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from colossalai.logging import get_dist_logger
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from colossalai.kernel.triton import (
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copy_k_to_blocked_cache,
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decoding_fused_rotary_embedding,
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rotary_embedding,
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)
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logger = get_dist_logger(__name__)
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inference_ops = InferenceOpsLoader().load()
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class AttentionContext(ABC):
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@abstractmethod
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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raise NotImplementedError
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@abstractmethod
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def decode(self, attn_metadata: AttentionMetaData, **kwargs):
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raise NotImplementedError
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class CudaAttentionContext(AttentionContext):
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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if not attn_metadata.use_spec_dec:
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if not attn_metadata.use_alibi_attn:
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inference_ops.rotary_embedding(
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attn_metadata.query_states,
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attn_metadata.key_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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kwargs.get('high_precision', False),
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)
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inference_ops.context_kv_cache_memcpy(
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attn_metadata.key_states,
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attn_metadata.value_states,
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attn_metadata.k_cache,
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attn_metadata.v_cache,
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attn_metadata.sequence_lengths,
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attn_metadata.cu_seqlens,
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attn_metadata.block_tables,
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attn_metadata.kv_seq_len,
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)
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else:
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rotary_embedding(
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attn_metadata.query_states,
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attn_metadata.key_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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)
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def decode(self, attn_metadata: AttentionMetaData, **kwargs):
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if attn_metadata.use_alibi_attn:
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inference_ops.rotary_embedding_and_cache_copy(
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attn_metadata.query_states,
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attn_metadata.key_states,
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attn_metadata.value_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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attn_metadata.k_cache,
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attn_metadata.v_cache,
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attn_metadata.sequence_lengths,
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attn_metadata.block_tables,
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attn_metadata.high_precision,
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)
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else:
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inference_ops.decode_kv_cache_memcpy(
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attn_metadata.key_states,
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attn_metadata.value_states,
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attn_metadata.k_cache,
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attn_metadata.v_cache,
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attn_metadata.sequence_lengths,
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attn_metadata.block_tables,
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)
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class TritonAttentionContext(AttentionContext):
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def prefill(self, attn_metadata: AttentionMetaData, **kwargs):
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if not attn_metadata.use_alibi_attn:
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rotary_embedding(
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attn_metadata.query_states,
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attn_metadata.key_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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)
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def decode(self, attn_metadata: AttentionMetaData, **kwargs):
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if not attn_metadata.use_spec_dec and not attn_metadata.use_alibi_attn:
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decoding_fused_rotary_embedding(
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attn_metadata.query_states,
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attn_metadata.key_states,
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attn_metadata.value_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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attn_metadata.k_cache,
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attn_metadata.v_cache,
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attn_metadata.block_tables,
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attn_metadata.sequence_lengths,
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)
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else:
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if not attn_metadata.use_alibi_attn:
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rotary_embedding(
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attn_metadata.query_states,
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attn_metadata.key_states,
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kwargs.get('cos', None),
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kwargs.get('sin', None),
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)
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copy_k_to_blocked_cache(
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attn_metadata.key_states,
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attn_metadata.k_cache,
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kv_lengths=attn_metadata.sequence_lengths,
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block_tables=attn_metadata.block_tables,
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n=kwargs.get('q_len', 1)
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)
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copy_k_to_blocked_cache(
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attn_metadata.value_states,
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attn_metadata.v_cache,
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kv_lengths=attn_metadata.sequence_lengths,
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block_tables=attn_metadata.block_tables,
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n=kwargs.get('q_len', 1)
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)
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def get_attention_context(use_spec_dec: bool, use_cuda_kernel: bool, dtype: torch.dtype) -> AttentionContext:
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use_flash_attn = can_use_flash_attn2(dtype)
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if use_cuda_kernel and use_flash_attn and not use_spec_dec:
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return CudaAttentionContext()
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else:
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return TritonAttentionContext()
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@@ -8,6 +8,7 @@ import torch.nn as nn
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from torch.distributed import ProcessGroup
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from colossalai.inference.flash_decoding_utils import FDIntermTensors
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from colossalai.inference.utils import get_alibi_slopes
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from colossalai.inference.modeling.models.nopadding_llama import NopadLlamaMLP
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from colossalai.kernel.kernel_loader import InferenceOpsLoader
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from colossalai.kernel.triton import (
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@@ -47,22 +48,6 @@ inference_ops = InferenceOpsLoader().load()
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logger = get_dist_logger(__name__)
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# alibi slopes calculation adapted from https://github.com/huggingface/transformers/blob/v4.36.0/src/transformers/models/bloom/modeling_bloom.py#L57
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def get_alibi_slopes(num_heads: int, device: torch.device) -> torch.Tensor:
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closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
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base = torch.tensor(2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), dtype=torch.float32, device=device)
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powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32, device=device)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), dtype=torch.float32, device=device
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)
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num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
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extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, dtype=torch.int32, device=device)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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return slopes
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def baichuan_rmsnorm_forward(
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self,
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hidden_states: torch.Tensor,
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@@ -18,6 +18,9 @@ from transformers.models.llama.modeling_llama import (
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from colossalai.inference.config import InputMetaData
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from colossalai.inference.flash_decoding_utils import FDIntermTensors
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from colossalai.inference.modeling.backends.attention_backend import get_attention_backend, AttentionMetaData
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from colossalai.inference.modeling.backends.attention_context import get_attention_context
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from colossalai.inference.utils import can_use_flash_attn2
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from colossalai.kernel.kernel_loader import InferenceOpsLoader
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from colossalai.kernel.triton import (
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context_attention_unpadded,
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@@ -36,14 +39,6 @@ inference_ops = InferenceOpsLoader().load()
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logger = get_dist_logger(__name__)
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try:
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from flash_attn import flash_attn_varlen_func
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use_flash_attn2 = True
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except ImportError:
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use_flash_attn2 = False
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logger.warning(f"flash_attn2 has not been installed yet, we will use triton flash attn instead.")
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def llama_causal_lm_forward(
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self: LlamaForCausalLM,
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@@ -126,7 +121,7 @@ def llama_model_forward(
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cos_sin = (self._cos_cached[rotary_indexes], self._sin_cached[rotary_indexes])
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elif use_cuda_kernel:
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if inputmetadata.dtype != torch.float32 and use_flash_attn2:
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if inputmetadata.dtype != torch.float32 and can_use_flash_attn2():
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cu_seqlens = F.pad(torch.cumsum(sequence_lengths, dim=0, dtype=torch.torch.int32), (1, 0))
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hidden_dim = self._cos_cached.size(-1)
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@@ -532,112 +527,54 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
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)
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block_size = k_cache.size(-2)
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if is_prompts:
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if not is_verifier and use_cuda_kernel and query_states.dtype != torch.float32 and use_flash_attn2:
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# flash attn 2 currently only supports FP16/BF16.
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inference_ops.rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1], high_precision)
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inference_ops.context_kv_cache_memcpy(
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key_states, value_states, k_cache, v_cache, sequence_lengths, cu_seqlens, block_tables, kv_seq_len
|
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)
|
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attn_output = flash_attn_varlen_func(
|
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query_states,
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key_states,
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value_states,
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cu_seqlens_q=cu_seqlens,
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cu_seqlens_k=cu_seqlens,
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max_seqlen_q=kv_seq_len,
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max_seqlen_k=kv_seq_len,
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dropout_p=0.0,
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softmax_scale=sm_scale,
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causal=True,
|
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)
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attn_output = attn_output.view(token_nums, -1)
|
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else:
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rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
|
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attn_output = context_attention_unpadded(
|
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q=query_states,
|
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k=key_states,
|
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v=value_states,
|
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k_cache=k_cache,
|
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v_cache=v_cache,
|
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context_lengths=sequence_lengths,
|
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block_tables=block_tables,
|
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block_size=block_size,
|
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output=output_tensor,
|
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max_seq_len=kv_seq_len,
|
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sm_scale=sm_scale,
|
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use_new_kcache_layout=use_cuda_kernel,
|
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)
|
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else:
|
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|
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attn_metadata = AttentionMetaData(
|
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query_states=query_states,
|
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key_states=key_states,
|
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value_states=value_states,
|
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k_cache=k_cache,
|
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v_cache=v_cache,
|
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block_tables=block_tables,
|
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block_size=block_size,
|
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kv_seq_len=kv_seq_len,
|
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sequence_lengths=sequence_lengths,
|
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sm_scale=sm_scale,
|
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alibi_slopes=None,
|
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cu_seqlens=cu_seqlens,
|
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output_tensor=output_tensor,
|
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use_spec_dec=is_verifier,
|
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use_alibi_attn=False,
|
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)
|
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|
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attention_backend = get_attention_backend(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
|
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attention_context = get_attention_context(use_spec_dec=is_verifier, use_cuda_kernel=use_cuda_kernel, dtype=query_states.dtype)
|
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|
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if is_prompts: # prefilling stage
|
||||
attention_context.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:
|
||||
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,
|
||||
)
|
||||
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,
|
||||
None,
|
||||
sm_scale,
|
||||
)
|
||||
attn_output = output_tensor
|
||||
else:
|
||||
if is_verifier:
|
||||
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
|
||||
)
|
||||
else:
|
||||
decoding_fused_rotary_embedding(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
cos_sin[0],
|
||||
cos_sin[1],
|
||||
k_cache,
|
||||
v_cache,
|
||||
block_tables,
|
||||
sequence_lengths,
|
||||
)
|
||||
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,
|
||||
sm_scale=sm_scale,
|
||||
kv_group_num=self.num_key_value_groups,
|
||||
q_len=q_len,
|
||||
)
|
||||
|
||||
attention_context.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,
|
||||
num_key_value_groups=self.num_key_value_groups,
|
||||
q_len=q_len,
|
||||
)
|
||||
|
||||
attn_output = attn_output.view(-1, self.hidden_size)
|
||||
attn_output = self.o_proj(attn_output)
|
||||
@@ -695,3 +632,4 @@ class NopadLlamaAttention(LlamaAttention, ParallelModule):
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return f"qkv_weight_proj MergedLinear1D_Col: in_features={self.qkv_weight.shape[1]}x3, out_features={self.qkv_weight.shape[2]}, bias=False"
|
||||
|
||||
Reference in New Issue
Block a user