136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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from cacheflow import ops
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from cacheflow.models import InputMetadata
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class OPTCacheFlowAttention(nn.Module):
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def __init__(self, scale: float) -> None:
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super().__init__()
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self.scale = scale
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def _masked_attention(
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self,
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query: torch.Tensor, # [num_queries, num_heads, head_size]
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key: torch.Tensor, # [num_keys, num_heads, head_size]
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value: torch.Tensor, # [num_keys, num_heads, head_size]
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attn_mask: Optional[torch.Tensor] = None, # [num_queries, num_keys]
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) -> torch.Tensor: # [num_queries, num_heads, head_size]
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query = query * self.scale
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attn = torch.einsum('qhd,khd->hqk', query, key)
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if attn_mask is not None:
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attn = attn + attn_mask
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attn = torch.softmax(attn, dim=-1)
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out = torch.einsum('hqk,khd->qhd', attn, value)
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return out
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def multi_query_kv_attention(
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self,
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output: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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) -> None:
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# FIXME(woosuk): Replace this with a custom op call.
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attention_mask = torch.triu(
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torch.ones(query.shape[0], key.shape[0]), diagonal=1) * -1e5
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attention_mask = attention_mask.to(dtype=query.dtype, device=query.device)
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out = self._masked_attention(query, key, value, attention_mask)
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output.copy_(out, non_blocking=True)
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def single_query_cached_kv_attention(
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self,
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output: torch.Tensor,
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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input_metadata: InputMetadata,
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) -> None:
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num_heads = value_cache.shape[1]
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head_size = value_cache.shape[3]
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block_size = value_cache.shape[2]
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block_tables = input_metadata.block_tables
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# FIXME(woosuk): Replace the following with a custom op.
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for i in range(input_metadata.num_generation_tokens):
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q = query[i].unsqueeze(0)
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block_table = block_tables[i]
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context_len = int(input_metadata.context_lens[i])
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keys = []
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values = []
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for j in range(context_len):
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block_number = int(block_table[j // block_size])
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block_offset = j % block_size
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k = key_cache[block_number, :, :, block_offset, :]
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k = k.reshape(num_heads, head_size)
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keys.append(k)
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v = value_cache[block_number, :, block_offset, :]
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values.append(v)
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keys = torch.stack(keys, dim=0)
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values = torch.stack(values, dim=0)
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out = self._masked_attention(q, keys, values)
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out = out.view(num_heads, head_size)
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output[i].copy_(out, non_blocking=True)
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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# Prune out invalid tokens.
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query = query[:input_metadata.num_valid_tokens]
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key = key[:input_metadata.num_valid_tokens]
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value = value[:input_metadata.num_valid_tokens]
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# Reshape the input tensors.
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num_heads = value_cache.shape[1]
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head_size = value_cache.shape[3]
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query = query.view(-1, num_heads, head_size)
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key = key.view(-1, num_heads, head_size)
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value = value.view(-1, num_heads, head_size)
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# Compute the attention op for prompts.
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output = torch.empty_like(query)
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start_idx = 0
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for i in range(input_metadata.num_prompts):
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prompt_len = input_metadata.prompt_lens[i]
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out = output[start_idx:start_idx + prompt_len]
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q = query[start_idx:start_idx + prompt_len]
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k = key[start_idx:start_idx + prompt_len]
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v = value[start_idx:start_idx + prompt_len]
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self.multi_query_kv_attention(out, q, k, v)
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start_idx += prompt_len
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# Wait until the cache op is done.
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if cache_event is not None:
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cache_event.wait()
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# Reshape the keys and values and store them in the cache.
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ops.reshape_and_cache(
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key, value, key_cache, value_cache, input_metadata.slot_mapping)
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if input_metadata.num_generation_tokens > 0:
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# Compute the attention op for generation tokens.
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self.single_query_cached_kv_attention(
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output[start_idx:],
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query[start_idx:],
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key_cache,
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value_cache,
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input_metadata)
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# Reshape the output tensor.
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return output.view(-1, num_heads * head_size)
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