mirror of https://github.com/hpcaitech/ColossalAI
fix bugs related to processing padding mask
yuehuayingxueluo
FrankLeeeee
parent
e545a871b8
commit
2a73e828eb
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@ -196,6 +196,7 @@ class PagedAttention:
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v_cache: torch.Tensor,
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context_lengths: torch.Tensor, # [num_seqs]
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block_tables: torch.Tensor, # [num_seqs,max_blocks_per_sequence]
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attn_mask: torch.Tensor = None, # [bsz, input_lengths + output_lengths]
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):
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# Firt, do shape verification
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bsz, seq_len, num_heads, head_size = q.shape
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@ -205,8 +206,6 @@ class PagedAttention:
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block_size = k_cache.shape[-1]
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assert q.shape[0] == k.shape[0] == v.shape[0] == block_tables.shape[0]
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block_tables.shape[-1] * block_size
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shape = (bsz, seq_len, num_heads, head_size)
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input_shape = shape[:2]
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# Copy kv to memory(rotary embedded)
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copy_to_cache(k, k_cache, lengths=context_lengths, block_tables=block_tables)
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@ -217,8 +216,16 @@ class PagedAttention:
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v = PagedAttention.repeat_kv(v.transpose(1, 2), num_kv_groups)
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attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
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attn_mask = AttentionMaskConverter._make_causal_mask(input_shape, q.dtype, q.device, past_key_values_length=0)
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attn_mask = attn_mask + PagedAttention.generate_padding_mask(context_lengths, seq_len)
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if attn_mask is not None:
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padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, seq_len)
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attn_mask = AttentionMaskConverter._make_causal_mask(
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(bsz, seq_len), q.dtype, q.device, past_key_values_length=seq_len - seq_len
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)
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if padding_mask is not None:
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attn_mask = attn_mask.masked_fill(padding_mask.bool(), torch.finfo(q.dtype).min)
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if attn_weights.size() != (bsz, num_heads, seq_len, seq_len):
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raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,seq_len,seq_len)}.")
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@ -246,27 +253,17 @@ class PagedAttention:
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v_cache: torch.Tensor,
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lengths: torch.Tensor, # [num_seqs]: input_lengths + output_lengths
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block_tables: torch.Tensor, # [num_seqs,max_blocks_per_sequence]
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attn_mask: torch.Tensor = None, # [bsz, input_lengths + output_lengths]
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):
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# Firt, do shape verification.
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bsz, _, num_heads, head_size = q.shape
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bsz, q_length, num_heads, head_size = q.shape
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num_kv_heads = k.shape[-2]
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assert num_heads % num_kv_heads == 0, "num_kv_heads should be divisible by num_heads"
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num_kv_groups = num_heads // num_kv_heads
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block_size = k_cache.shape[-1]
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seq_len = max(lengths)
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assert q.shape[0] == k.shape[0] == v.shape[0] == block_tables.shape[0]
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block_tables.shape[-1] * block_size
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attn_mask = AttentionMaskConverter._make_causal_mask(
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q.shape[:2], q.dtype, q.device, past_key_values_length=seq_len - 1
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)
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attn_mask = attn_mask + PagedAttention.generate_padding_mask(lengths, seq_len).unsqueeze(1).unsqueeze(2)
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# cos, sin = self.rotary_emb(v, max_seq_len)
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# position_ids = lengths - 1
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# position_ids = position_ids.unsqueeze(1)
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# query, key = apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=2)
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copy_to_cache(k, k_cache, lengths=lengths, block_tables=block_tables, type="decoding")
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copy_to_cache(v, v_cache, lengths=lengths, block_tables=block_tables, type="decoding")
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@ -283,8 +280,16 @@ class PagedAttention:
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raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,1,seq_len)}.")
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if attn_mask is not None:
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attn_weights += attn_mask
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padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, query_length)
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attn_mask = AttentionMaskConverter._make_causal_mask(
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(bsz, q_length), q.dtype, q.device, past_key_values_length=seq_len - query_length
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)
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if padding_mask is not None:
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attn_mask = attn_mask.masked_fill(padding_mask.bool(), torch.finfo(q.dtype).min)
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attn_weights += attn_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
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attn_output = torch.matmul(attn_weights, v)
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@ -1,10 +1,7 @@
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# This code is adapted from huggingface transformers: https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/llama/modeling_llama.py
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import math
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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from transformers.models.llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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@ -13,10 +10,10 @@ from transformers.models.llama.modeling_llama import (
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repeat_kv,
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)
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from colossalai.inference.modeling.layers.attention import convert_kvcache, copy_to_cache
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from colossalai.inference.modeling.layers.attention import PagedAttention
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from colossalai.inference.struct import BatchInfo
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from flash_attn.bert_padding import index_first_axis # noqa
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from flash_attn.bert_padding import index_first_axis, pad_input # noqa
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def rotate_half(x):
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@ -163,11 +160,11 @@ def llama_attn_forward(
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value_states = value_states.transpose(1, 2)
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if is_prompts:
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attn_output = pad_context_forward(
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attn_output = PagedAttention.pad_context_forward(
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query_states, key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables, attention_mask
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)
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else:
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attn_output = pad_decoding_forward(
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attn_output = PagedAttention.pad_decoding_forward(
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query_states, key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables, attention_mask
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)
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@ -182,118 +179,3 @@ def generate_padding_position_id(attention_mask: torch.Tensor) -> torch.Tensor:
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position_ids = attention_mask.long().cumsum(-1) - 1
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position_ids.masked_fill_(attention_mask == 0, 1)
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return position_ids
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def unpading_input(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attention_mask: torch.Tensor):
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seqlens = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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batch_size, kv_seq_len, num_key_value_heads, head_dim = q.shape
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q = index_first_axis(q.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices)
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k = index_first_axis(k.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices)
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v = index_first_axis(v.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices)
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return (q, k, v, indices, seqlens)
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def pad_decoding_forward(
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query: torch.Tensor, # [bsz, 1, num_heads, head_size]
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key: torch.Tensor,
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value: torch.Tensor,
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k_cache: torch.Tensor, # [num_blocks, num_heads, head_size, block_size]
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v_cache: torch.Tensor,
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lengths: torch.Tensor, # [num_seqs]: input_lengths + output_lengths
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block_tables: torch.Tensor, # [num_seqs,max_blocks_per_sequence]
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attn_mask: torch.Tensor = None,
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):
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bsz, query_length, num_heads, head_size = query.shape
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seq_len = max(lengths)
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copy_to_cache(key, k_cache, lengths=lengths, block_tables=block_tables, type="decoding")
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copy_to_cache(value, v_cache, lengths=lengths, block_tables=block_tables, type="decoding")
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key = convert_kvcache(k_cache, lengths, block_tables) # bsz, seqlen,
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value = convert_kvcache(v_cache, lengths, block_tables)
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query = query.transpose(1, 2)
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key = key.transpose(1, 2)
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value = value.transpose(1, 2)
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attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(head_size)
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if attn_weights.size() != (bsz, num_heads, 1, seq_len):
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raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,1,seq_len)}.")
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if attn_mask is not None:
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padding_mask = AttentionMaskConverter._expand_mask(attn_mask, query.dtype, query_length)
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attn_mask = AttentionMaskConverter._make_causal_mask(
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(bsz, query_length), query.dtype, query.device, past_key_values_length=seq_len - query_length
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)
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if padding_mask is not None:
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attn_mask = attn_mask.masked_fill(padding_mask.bool(), torch.finfo(query.dtype).min)
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attn_weights += attn_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_output = torch.matmul(attn_weights, value)
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if attn_output.size() != (bsz, num_heads, 1, head_size):
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raise ValueError(f"Got wrong attn_output, should be in shape {(bsz,num_heads,1,head_size)}.")
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attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, 1, -1)
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return attn_output
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def pad_context_forward(
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q: torch.Tensor, # [batch_size, seq_len, num_heads, head_size]
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k: torch.Tensor, # [batch_size, seq_len, num_kv_heads, head_size]
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v: torch.Tensor,
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k_cache: torch.Tensor, # [num_blocks, num_heads, head_size, block_size]
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v_cache: torch.Tensor,
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context_lengths: torch.Tensor, # [num_seqs]
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block_tables: torch.Tensor, # [num_seqs,max_blocks_per_sequence]
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attn_mask: torch.Tensor = None,
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):
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# Firt, do shape verification
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bsz, seq_len, num_heads, head_size = q.shape
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num_kv_heads = k.shape[-2]
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assert num_heads % num_kv_heads == 0, "num_kv_heads should be divisible by num_heads"
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num_kv_groups = num_heads // num_kv_heads
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block_size = k_cache.shape[-1]
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assert q.shape[0] == k.shape[0] == v.shape[0] == block_tables.shape[0]
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block_tables.shape[-1] * block_size
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# Copy kv to memory(rotary embedded)
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copy_to_cache(k, k_cache, lengths=context_lengths, block_tables=block_tables)
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copy_to_cache(v, v_cache, lengths=context_lengths, block_tables=block_tables)
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q = q.transpose(1, 2)
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k = repeat_kv(k.transpose(1, 2), num_kv_groups)
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v = repeat_kv(v.transpose(1, 2), num_kv_groups)
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attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(head_size)
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if attn_mask is not None:
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padding_mask = AttentionMaskConverter._expand_mask(attn_mask, q.dtype, seq_len)
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attn_mask = AttentionMaskConverter._make_causal_mask(
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(bsz, seq_len), q.dtype, q.device, past_key_values_length=seq_len - seq_len
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)
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if padding_mask is not None:
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attn_mask = attn_mask.masked_fill(padding_mask.bool(), torch.finfo(q.dtype).min)
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if attn_weights.size() != (bsz, num_heads, seq_len, seq_len):
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raise ValueError(f"Got wrong attn_weights, should be in shape {(bsz,num_heads,seq_len,seq_len)}.")
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if attn_mask is not None:
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attn_weights += attn_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype)
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attn_output = torch.matmul(attn_weights, v)
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if attn_output.size() != (bsz, num_heads, seq_len, head_size):
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raise ValueError(f"Got wrong attn_output, should be in shape {(bsz,num_heads,seq_len,head_size)}.")
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attn_output = attn_output.transpose(1, 2).contiguous().reshape(bsz, seq_len, -1)
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del attn_weights
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return attn_output
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