297 lines
12 KiB
Python
297 lines
12 KiB
Python
import logging
|
|
import torch
|
|
from torch import nn
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func, flash_attn_with_kvcache
|
|
from nanovllm.utils.context import get_context
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@triton.jit
|
|
def store_kvcache_kernel(
|
|
key_ptr,
|
|
key_stride,
|
|
value_ptr,
|
|
value_stride,
|
|
k_cache_ptr,
|
|
v_cache_ptr,
|
|
slot_mapping_ptr,
|
|
D: tl.constexpr,
|
|
):
|
|
idx = tl.program_id(0)
|
|
slot = tl.load(slot_mapping_ptr + idx)
|
|
if slot == -1: return
|
|
key_offsets = idx * key_stride + tl.arange(0, D)
|
|
value_offsets = idx * value_stride + tl.arange(0, D)
|
|
key = tl.load(key_ptr + key_offsets)
|
|
value = tl.load(value_ptr + value_offsets)
|
|
cache_offsets = slot * D + tl.arange(0, D)
|
|
tl.store(k_cache_ptr + cache_offsets, key)
|
|
tl.store(v_cache_ptr + cache_offsets, value)
|
|
|
|
|
|
def store_kvcache(key: torch.Tensor, value: torch.Tensor, k_cache: torch.Tensor, v_cache: torch.Tensor, slot_mapping: torch.Tensor):
|
|
N, num_heads, head_dim = key.shape
|
|
D = num_heads * head_dim
|
|
assert key.stride(-1) == 1 and value.stride(-1) == 1
|
|
assert key.stride(1) == head_dim and value.stride(1) == head_dim
|
|
assert k_cache.stride(1) == D and v_cache.stride(1) == D
|
|
assert slot_mapping.numel() == N
|
|
store_kvcache_kernel[(N,)](key, key.stride(0), value, value.stride(0), k_cache, v_cache, slot_mapping, D)
|
|
|
|
|
|
class Attention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
num_heads,
|
|
head_dim,
|
|
scale,
|
|
num_kv_heads,
|
|
):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
self.head_dim = head_dim
|
|
self.scale = scale
|
|
self.num_kv_heads = num_kv_heads
|
|
self.k_cache = self.v_cache = torch.tensor([])
|
|
# Layer ID set by model_runner after model creation
|
|
self.layer_id: int = -1
|
|
|
|
def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
|
|
context = get_context()
|
|
k_cache, v_cache = self.k_cache, self.v_cache
|
|
if k_cache.numel() and v_cache.numel():
|
|
store_kvcache(k, v, k_cache, v_cache, context.slot_mapping)
|
|
|
|
if context.is_prefill:
|
|
if context.is_chunked_prefill:
|
|
# Chunked prefill: merge attention from previous KV
|
|
o = self._chunked_prefill_attention(q, k, v, context)
|
|
elif context.block_tables is not None: # prefix cache
|
|
k, v = k_cache, v_cache
|
|
o = flash_attn_varlen_func(q, k, v,
|
|
max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
|
|
max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
|
|
softmax_scale=self.scale, causal=True, block_table=context.block_tables)
|
|
else:
|
|
o = flash_attn_varlen_func(q, k, v,
|
|
max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
|
|
max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
|
|
softmax_scale=self.scale, causal=True, block_table=context.block_tables)
|
|
else: # decode
|
|
if context.is_chunked_prefill:
|
|
# Chunked decode: need to load all KV from CPU+GPU
|
|
o = self._chunked_decode_attention(q, k, v, context)
|
|
else:
|
|
o = flash_attn_with_kvcache(q.unsqueeze(1), k_cache, v_cache,
|
|
cache_seqlens=context.context_lens, block_table=context.block_tables,
|
|
softmax_scale=self.scale, causal=True)
|
|
return o
|
|
|
|
def _chunked_prefill_attention(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
context,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Compute attention with three-region GPU buffer for chunked prefill.
|
|
|
|
For chunked prefill:
|
|
1. Load previous KV from CPU using Compute/Prefetch region (if any previous chunks)
|
|
2. Compute attention against previous KV chunks (no causal mask)
|
|
3. Compute attention against current chunk's KV (causal)
|
|
4. Merge all results using online softmax
|
|
|
|
Three-region design guarantees: current chunk's KV is in Compute region, previous KV is loaded
|
|
from CPU to Prefetch region, so write and load regions never overlap.
|
|
"""
|
|
from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
|
|
|
|
# q, k, v shape: [total_tokens, num_heads, head_dim]
|
|
# Reshape for flash attention: [batch, seq, heads, dim]
|
|
q_batched = q.unsqueeze(0) # [1, total_tokens, heads, dim]
|
|
k_batched = k.unsqueeze(0)
|
|
v_batched = v.unsqueeze(0)
|
|
|
|
o_acc = None
|
|
lse_acc = None
|
|
|
|
# Load previous KV from CPU using Compute/Prefetch region
|
|
# Note: context.offload_engine is actually HybridKVCacheManager
|
|
kvcache_manager = context.offload_engine
|
|
seq = context.chunked_seq if hasattr(context, 'chunked_seq') else None
|
|
|
|
if kvcache_manager is not None and seq is not None and self.layer_id >= 0:
|
|
# Get prefilled CPU blocks (blocks already written in previous chunks)
|
|
cpu_block_table = kvcache_manager.get_prefilled_cpu_blocks(seq)
|
|
|
|
if cpu_block_table:
|
|
offload_engine = kvcache_manager.offload_engine
|
|
# Use Prefetch region to load previous KV (won't conflict with current Compute region)
|
|
prefetch_size = offload_engine.num_prefetch_blocks
|
|
num_chunks = (len(cpu_block_table) + prefetch_size - 1) // prefetch_size
|
|
|
|
for chunk_idx in range(num_chunks):
|
|
start = chunk_idx * prefetch_size
|
|
end = min(start + prefetch_size, len(cpu_block_table))
|
|
num_blocks_in_chunk = end - start
|
|
chunk_ids = cpu_block_table[start:end]
|
|
|
|
# Load this chunk to Prefetch region (per-layer loading)
|
|
# Each layer loads only its own KV, avoiding the bug where layer 0
|
|
# loads all layers and overwrites data before other layers can read it
|
|
offload_engine.load_to_prefetch_layer(self.layer_id, chunk_ids)
|
|
|
|
# Wait for this layer's Prefetch region and get KV
|
|
offload_engine.wait_prefetch_layer(self.layer_id)
|
|
prev_k, prev_v = offload_engine.get_kv_for_prefetch(
|
|
self.layer_id, num_blocks_in_chunk
|
|
)
|
|
|
|
# Compute attention against this chunk (no causal mask)
|
|
prev_o, prev_lse = flash_attn_with_lse(
|
|
q_batched,
|
|
prev_k,
|
|
prev_v,
|
|
softmax_scale=self.scale,
|
|
causal=False,
|
|
)
|
|
|
|
# Merge with accumulated
|
|
if o_acc is None:
|
|
o_acc, lse_acc = prev_o, prev_lse
|
|
else:
|
|
o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
|
|
|
|
# Compute attention against current chunk's KV (with causal mask)
|
|
current_o, current_lse = flash_attn_with_lse(
|
|
q_batched,
|
|
k_batched,
|
|
v_batched,
|
|
softmax_scale=self.scale,
|
|
causal=True,
|
|
)
|
|
|
|
# Merge with accumulated
|
|
if o_acc is None:
|
|
final_o = current_o
|
|
else:
|
|
final_o, _ = merge_attention_outputs(o_acc, lse_acc, current_o, current_lse)
|
|
|
|
# Remove batch dimension: [1, total_tokens, heads, dim] -> [total_tokens, heads, dim]
|
|
return final_o.squeeze(0)
|
|
|
|
def _chunked_decode_attention(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
context,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Compute decode attention with three-region GPU buffer.
|
|
|
|
All KV is stored on CPU. Uses Compute region buffer on GPU:
|
|
1. Load chunk to Compute region
|
|
2. Compute attention
|
|
3. Repeat for all chunks
|
|
4. Finally, attend to Decode region (slot 0) which contains the new token's KV
|
|
5. Merge all attention outputs using online softmax (LSE)
|
|
|
|
Key: new token's KV is in Decode region (slot 0), won't be overwritten by Compute region loading.
|
|
"""
|
|
from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
|
|
|
|
# q shape: [batch_size, num_heads, head_dim] (single decode token per sequence)
|
|
# Need: [batch, seqlen, heads, dim]
|
|
q_batched = q.unsqueeze(1) # [batch, 1, heads, dim]
|
|
|
|
# Note: context.offload_engine is actually HybridKVCacheManager
|
|
kvcache_manager = context.offload_engine
|
|
seq = context.chunked_seq
|
|
|
|
# Get all CPU blocks for this sequence
|
|
cpu_block_table, _ = kvcache_manager.get_all_cpu_blocks(seq)
|
|
if self.layer_id == 0:
|
|
logger.debug(f"Decode attention: cpu_block_table={cpu_block_table}, seq.block_table={list(seq.block_table)}")
|
|
if not cpu_block_table:
|
|
raise RuntimeError("Chunked decode attention failed: no CPU blocks available")
|
|
|
|
# Get the actual offload_engine for three-region operations
|
|
offload_engine = kvcache_manager.offload_engine
|
|
|
|
# Calculate chunk info using Compute region
|
|
compute_size = offload_engine.num_compute_blocks
|
|
num_chunks = (len(cpu_block_table) + compute_size - 1) // compute_size
|
|
|
|
o_acc = None
|
|
lse_acc = None
|
|
|
|
for chunk_idx in range(num_chunks):
|
|
start = chunk_idx * compute_size
|
|
end = min(start + compute_size, len(cpu_block_table))
|
|
num_blocks_in_chunk = end - start
|
|
chunk_ids = cpu_block_table[start:end]
|
|
|
|
# Load this chunk to Compute region (per-layer loading)
|
|
# Each layer loads only its own KV, avoiding the bug where layer 0
|
|
# loads all layers and overwrites data before other layers can read it
|
|
offload_engine.load_to_compute_layer(self.layer_id, chunk_ids)
|
|
|
|
# Wait for this layer's Compute region to be ready and get KV
|
|
offload_engine.wait_compute_layer(self.layer_id)
|
|
k_chunk, v_chunk = offload_engine.get_kv_for_compute(
|
|
self.layer_id, num_blocks_in_chunk
|
|
)
|
|
|
|
# Compute attention for this chunk
|
|
o_chunk, lse_chunk = flash_attn_with_lse(
|
|
q_batched, k_chunk, v_chunk,
|
|
softmax_scale=self.scale,
|
|
causal=False,
|
|
)
|
|
|
|
# Merge with accumulated
|
|
if o_acc is None:
|
|
o_acc, lse_acc = o_chunk, lse_chunk
|
|
else:
|
|
o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o_chunk, lse_chunk)
|
|
|
|
# Now attend to Decode region (contains accumulated decode tokens)
|
|
# When batching offloads, decode slot accumulates multiple tokens
|
|
# from decode_start_pos_in_block to decode_pos_in_block (inclusive)
|
|
pos_in_block = context.decode_pos_in_block
|
|
start_pos = context.decode_start_pos_in_block
|
|
num_accumulated = pos_in_block - start_pos + 1
|
|
|
|
if num_accumulated > 0:
|
|
# Get accumulated KV in decode slot [start_pos : pos_in_block+1]
|
|
decode_k = offload_engine.k_cache_gpu[self.layer_id, offload_engine.decode_slot, start_pos:pos_in_block+1]
|
|
decode_v = offload_engine.v_cache_gpu[self.layer_id, offload_engine.decode_slot, start_pos:pos_in_block+1]
|
|
decode_k = decode_k.unsqueeze(0) # [1, num_tokens, heads, dim]
|
|
decode_v = decode_v.unsqueeze(0)
|
|
|
|
decode_o, decode_lse = flash_attn_with_lse(
|
|
q_batched, decode_k, decode_v,
|
|
softmax_scale=self.scale,
|
|
causal=False,
|
|
)
|
|
|
|
# Merge with accumulated
|
|
if o_acc is None:
|
|
o_acc = decode_o
|
|
else:
|
|
o_acc, _ = merge_attention_outputs(o_acc, lse_acc, decode_o, decode_lse)
|
|
|
|
if o_acc is None:
|
|
raise RuntimeError("Chunked decode attention failed: no KV available")
|
|
|
|
# Output shape: [batch, 1, heads, dim] (same as normal decode)
|
|
return o_acc
|