Merge pull request #95 from casper-hansen/cache_refactor

Refactor cache and embedding modules

awq/modules/fused/attn.py

@@ -2,69 +2,88 @@
 import math
 import torch
 import torch.nn as nn
-import awq_inference_engine
 from torch.nn import functional as F
+from awq.modules.fused.cache import WindowedCache
+from awq.utils.fused_utils import get_attention_shapes
 
 try:
  import ft_inference_engine
  FT_INSTALLED = True
 except:
  FT_INSTALLED = False
 
-def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
- freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
- t = torch.arange(end, device=freqs.device) # type: ignore
- freqs = torch.outer(t, freqs).float() # type: ignore
- freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
- return freqs_cis
-
-def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
- ndim = x.ndim
- assert 0 <= 1 < ndim
- assert freqs_cis.shape == (x.shape[1], x.shape[-1])
- shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
- return freqs_cis.view(*shape)
-
-def apply_rotary_emb(
- xq: torch.Tensor,
- xk: torch.Tensor,
- freqs_cis: torch.Tensor,
-):
- xq_ = torch.view_as_complex(
- xq.float().reshape(*xq.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
- )
- xk_ = torch.view_as_complex(
- xk.float().reshape(*xk.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
- )
- freqs_cis = reshape_for_broadcast(freqs_cis, xq_).to(xq_.device)
- xq_out = torch.view_as_real(xq_ * freqs_cis).transpose(-2, -1).flatten(3)
- xk_out = torch.view_as_real(xk_ * freqs_cis).transpose(-2, -1).flatten(3)
- return xq_out.type_as(xq), xk_out.type_as(xk)
-
-def gen_slopes(n_heads, alibi_bias_max=8):
- _n_heads = 2 ** math.ceil(math.log2(n_heads))
- m = torch.arange(1, _n_heads + 1, dtype=torch.float32)
- m = m.mul(alibi_bias_max / _n_heads)
- slopes = 1.0 / torch.pow(2, m)
- if _n_heads != n_heads:
- slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
- return slopes.view(1, n_heads, 1, 1)
-
-
-def build_alibi_bias(
- n_heads, seq_len, full=False, alibi_bias_max=8, dtype=torch.float32
-):
- alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32).view(1, 1, 1, seq_len)
- if full:
- alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32).view(
- 1, 1, seq_len, 1
+class RoPE(nn.Module):
+ def __init__(self, hidden_size, n_heads, max_seq_len, device):
+ super(RoPE, self).__init__()
+
+ self.freqs_cis = nn.Parameter(
+ self.precompute_freqs_cis(hidden_size // n_heads, max_seq_len * 2).to(device),
+ requires_grad=False
+ )
+
+ @staticmethod
+ def precompute_freqs_cis(dim: int, end: int, theta=10000.0):
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+ t = torch.arange(end)
+ freqs = torch.outer(t, freqs).float()
+ freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+ return freqs_cis
+
+ @staticmethod
+ def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+ ndim = x.ndim
+ assert 0 <= 1 < ndim
+ assert freqs_cis.shape == (x.shape[1], x.shape[-1])
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ return freqs_cis.view(*shape)
+
+ def forward(self, xq: torch.Tensor, xk: torch.Tensor, start_pos: int, seqlen: int):
+ xq_ = torch.view_as_complex(
+ xq.float().reshape(*xq.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
  )
- alibi_bias = alibi_bias.abs().mul(-1)
- slopes = gen_slopes(n_heads, alibi_bias_max)
- alibi_bias = alibi_bias * slopes
- slopes = slopes.squeeze(0).squeeze(-1).squeeze(-1)
- return slopes.to(dtype=dtype), alibi_bias.to(dtype=dtype)
+ xk_ = torch.view_as_complex(
+ xk.float().reshape(*xk.shape[:-1], 2, -1).transpose(-2, -1).contiguous()
+ )
+ freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
+ freqs_cis = self.reshape_for_broadcast(freqs_cis, xq_).to(xq_.device)
+
+ xq_out = torch.view_as_real(xq_ * freqs_cis).transpose(-2, -1).flatten(3)
+ xk_out = torch.view_as_real(xk_ * freqs_cis).transpose(-2, -1).flatten(3)
+
+ return xq_out.type_as(xq), xk_out.type_as(xk)
 
+class ALiBi(nn.Module):
+ def __init__(self, n_heads, max_seq_len, device, alibi_bias_max=8):
+ super(ALiBi, self).__init__()
+
+ # Initialize ALiBi slopes and bias
+ slopes, bias = self.build_alibi_bias(n_heads, max_seq_len, alibi_bias_max=alibi_bias_max)
+ self.slopes = nn.Parameter(slopes.float().to(device), requires_grad=False)
+ self.bias = nn.Parameter(bias.float().to(device), requires_grad=False)
+
+ @staticmethod
+ def gen_slopes(n_heads, alibi_bias_max=8):
+ _n_heads = 2 ** math.ceil(math.log2(n_heads))
+ m = torch.arange(1, _n_heads + 1, dtype=torch.float32)
+ m = m.mul(alibi_bias_max / _n_heads)
+ slopes = 1.0 / torch.pow(2, m)
+
+ if _n_heads != n_heads:
+ slopes = torch.cat([slopes[1::2], slopes[::2]])[:n_heads]
+
+ return slopes.view(1, n_heads, 1, 1)
+
+ @staticmethod
+ def build_alibi_bias(n_heads, seq_len, alibi_bias_max=8, dtype=torch.float32):
+ alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32).view(1, 1, 1, seq_len)
+ slopes = ALiBi.gen_slopes(n_heads, alibi_bias_max)
+ alibi_bias = alibi_bias * slopes
+ slopes = slopes.squeeze(0).squeeze(-1).squeeze(-1)
+ return slopes.to(dtype=dtype), alibi_bias.to(dtype=dtype)
+
+ def forward(self, scores, seqlen):
+ scores += self.bias[..., :seqlen]
+ return scores
 
 class QuantAttentionFused(nn.Module):
  def __init__(self, hidden_size, n_heads, n_kv_heads, qkv_layer, o_proj, dev, max_seq_len, 
@@ -81,74 +100,27 @@ def __init__(self, hidden_size, n_heads, n_kv_heads, qkv_layer, o_proj, dev, max
  self.use_alibi = use_alibi
  self.cache_batch_size = int(os.getenv("AWQ_BATCH_SIZE", "1"))
  self.max_seq_len = max_seq_len
- self.attention_shapes = self._get_attention_shapes(attention_shapes, max_seq_len)
- self.cache_v = ( torch.zeros(self.attention_shapes["cache_v"]).to(dev).half() )
- self.cache_k = ( torch.zeros(self.attention_shapes["cache_k"]).to(dev).half() )
+
+ # attention shapes for self attention
+ self.attention_shapes = get_attention_shapes(
+ attention_shapes, max_seq_len, self.cache_batch_size, n_heads, n_kv_heads, self.head_dim
+ )
+ # cache store that rolls cache
+ self.cache = WindowedCache(
+ self.attention_shapes["cache_v"], self.attention_shapes["cache_k"], dev
+ )
 
  if use_alibi:
- alibi_slopes, alibi_bias = build_alibi_bias(self.n_heads, max_seq_len)
- self.alibi_slopes = alibi_slopes.float().to(dev)
- self.alibi_bias = alibi_bias.float().to(dev)
+ self.alibi = ALiBi(n_heads, max_seq_len, dev)
  self.rotary_dim = 0
  self.is_neox = False
  else:
- self.freqs_cis = precompute_freqs_cis(
- hidden_size // n_heads,
- max_seq_len * 2,
- ).to(dev)
+ self.alibi = None
+ self.rope = RoPE(hidden_size, n_heads, max_seq_len, dev)
  self.rotary_dim = self.head_dim
- self.alibi_slopes = None
  self.is_neox = True
 
- def _get_attention_shapes(self, attention_shapes, max_seq_len):
- if attention_shapes is not None:
- attention_shapes = attention_shapes
-
- elif self.n_kv_heads == 0:
- attention_shapes = {
- # following fastertransformer definition
- "cache_v": (self.cache_batch_size, self.n_heads, max_seq_len, self.head_dim,),
- # 8: pack 8 fp16 in FT, if fp32 then use 4
- "cache_k": (self.cache_batch_size, self.n_heads, self.head_dim // 8, max_seq_len, 8,),
- "xqkv_view": (-1, self.n_heads, self.head_dim),
- "xq_slice": lambda xqkv: xqkv[:, :, 0],
- "xk_slice": lambda xqkv: xqkv[:, :, 1],
- "xv_slice": lambda xqkv: xqkv[:, :, 2],
- "xq_view": (self.n_heads, self.head_dim),
- "xk_view": (self.n_heads, self.head_dim),
- "xv_view": (self.n_heads, self.head_dim),
- "xk_reshape": (self.n_heads, self.head_dim // 8, 8),
- "single_xq_view": (self.n_heads, self.head_dim),
- "single_xk_view": (self.n_heads, self.head_dim),
- "single_xv_view": (self.n_heads, self.head_dim)
- }
-
- else:
- attention_shapes = {
- # following fastertransformer definition
- "cache_v": (self.cache_batch_size, self.n_kv_heads, max_seq_len, self.head_dim,),
- # 8: pack 8 fp16 in FT, if fp32 then use 4
- "cache_k": (self.cache_batch_size, self.n_kv_heads, self.head_dim // 8, max_seq_len, 8,),
- "xqkv_view": (self.n_heads + self.n_kv_heads * 2, self.head_dim),
- "xq_slice": lambda xqkv: xqkv[:, :, 0 : self.n_heads],
- "xk_slice": lambda xqkv: xqkv[:, :, self.n_heads : (self.n_heads + self.n_kv_heads)],
- "xv_slice": lambda xqkv: xqkv[:, :, -self.n_kv_heads :],
- "xq_view": (self.n_heads, self.head_dim),
- "xk_view": (self.n_kv_heads, self.head_dim),
- "xv_view": (self.n_kv_heads, self.head_dim),
- "xk_reshape": (self.n_kv_heads, self.head_dim // 8, 8),
- "single_xq_view": (self.n_heads, self.head_dim),
- "single_xk_view": (self.n_kv_heads, self.head_dim),
- "single_xv_view": (self.n_kv_heads, self.head_dim)
- }
-
- return attention_shapes
-
- def forward(
- self,
- hidden_states:torch.Tensor, past_key_value=None, attention_mask=None, position_ids=None, 
- output_attentions=False, use_cache=False, *args, **kwargs
- ):
+ def forward(self, hidden_states:torch.Tensor, attention_mask=None, *args, **kwargs):
  bsz, seqlen, _ = hidden_states.shape
  if bsz != self.cache_batch_size:
  raise RuntimeError(
@@ -157,14 +129,8 @@ def forward(
  )
 
  if self.start_pos > self.max_seq_len or self.start_pos + seqlen > self.max_seq_len:
- # Roll cache to the left
- roll_len = self.start_pos
- self.cache_v = torch.roll(self.cache_v, shifts=-roll_len, dims=2)
- self.cache_k = torch.roll(self.cache_k, shifts=-roll_len, dims=3)
- # Zero out the new part
- self.cache_v[:, :, -roll_len:, :] = 0
- self.cache_k[:, :, :, -roll_len:, :] = 0
- self.start_pos = 0
+ excess_length = self.start_pos + seqlen - self.max_seq_len
+ self.start_pos = self.cache.roll_kv(excess_length, self.start_pos)
 
  xqkv = self.qkv_proj(hidden_states)
  xqkv = xqkv.view((bsz, seqlen) + self.attention_shapes["xqkv_view"])
@@ -179,10 +145,9 @@ def forward(
  xv = xv.view((bsz, seqlen) + self.attention_shapes["xv_view"])
 
  if not self.use_alibi:
- xq, xk = apply_rotary_emb(xq, xk, freqs_cis=self.freqs_cis[self.start_pos : self.start_pos + seqlen])
+ xq, xk = self.rope.forward(xq, xk, self.start_pos, seqlen)
 
- self.cache_k = self.cache_k.to(xq)
- self.cache_v = self.cache_v.to(xq)
+ self.cache.to(xq)
 
  values_store = xv.transpose(2, 1)
  keys_store = (
@@ -191,13 +156,10 @@ def forward(
  .contiguous()
  )
 
- self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :] = values_store
- self.cache_k[:bsz, :, :, self.start_pos : self.start_pos + seqlen, :] = keys_store
+ self.cache.update_kv(values_store, keys_store, bsz, self.start_pos, seqlen)
 
  if seqlen == 1:
- xv = self.cache_v[:bsz, :, : self.start_pos + seqlen, :].transpose(1, 2).contiguous()
- xk = self.cache_k[:bsz, :, :, : self.start_pos + seqlen, :].transpose(2, 3).contiguous()
- xk = xk.reshape(xk.shape[:-2] + (self.head_dim,)).transpose(1, 2).contiguous()
+ xv, xk = self.cache.get_kv(bsz, self.start_pos, seqlen, self.head_dim)
 
  keys = xk
  values = xv
@@ -212,7 +174,7 @@ def forward(
  scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
 
  if self.use_alibi:
- scores += self.alibi_bias[..., :seqlen]
+ scores = self.alibi.forward(scores, seqlen)
 
  if attention_mask is not None:
  scores = scores + attention_mask # (bs, n_local_heads, slen, cache_len + slen)
@@ -225,14 +187,15 @@ def forward(
  xk = xk.view((bsz,) + self.attention_shapes["single_xk_view"])
  xv = xv.view((bsz,) + self.attention_shapes["single_xv_view"])
 
+ alibi_slopes = self.alibi.slopes if self.alibi is not None else None
  attention_weight = ft_inference_engine.single_query_attention(
  xq, # query
  xk, # key
  xv, # value
- self.cache_k, # key cache
- self.cache_v, # value cache
+ self.cache.k, # key cache
+ self.cache.v, # value cache
  None, # length per sample
- self.alibi_slopes, # alibi slopes
+ alibi_slopes, # alibi slopes
  self.start_pos, # timestep
  self.rotary_dim, # rotary embedding dimension
  10000, # rotary embedding base
@@ -241,11 +204,8 @@ def forward(
  attention_weight = attention_weight.reshape(bsz, 1, -1)
 
  attn_output = self.o_proj(attention_weight)
-
- if use_cache:
- self.start_pos += seqlen
- else:
- self.start_pos = 0
+ self.start_pos += seqlen
 
- # past_key_value is replaced with cache_v, cache_k, returning None
- return attn_output, attention_weight, None
+ # past_key_value is replaced with cache_v, cache_k, returning empty data
+ past_key_value = [torch.Tensor([ [ [[0]], [[0]], [[0]] ] ])]
+ return attn_output, attention_weight, past_key_value

awq/modules/fused/cache.py

@@ -0,0 +1,39 @@
+import torch
+
+class WindowedCache:
+ def __init__(self, cache_v_shape, cache_k_shape, device):
+ """
+ The window size is the same as the max_new_tokens. The window will
+ automatically roll once max_new_tokens is exceeded.
+ """
+ # [batch_size, n_kv_heads, max_seq_len, head_dim]
+ self.v = torch.zeros(cache_v_shape).to(device).half()
+ # [batch_size, n_kv_heads, head_dim // pack_factor, max_seq_len, pack_factor]
+ self.k = torch.zeros(cache_k_shape).to(device).half()
+
+ def get_kv(self, batch_size, start_pos, seqlen, head_dim):
+ xv = self.v[:batch_size, :, : start_pos + seqlen, :].transpose(1, 2).contiguous()
+ xk = self.k[:batch_size, :, :, : start_pos + seqlen, :].transpose(2, 3).contiguous()
+ xk = xk.reshape(xk.shape[:-2] + (head_dim,)).transpose(1, 2).contiguous()
+
+ return xv, xk
+
+ def update_kv(self, values_store, keys_store, batch_size, start_pos, seqlen):
+ self.v[:batch_size, :, start_pos : start_pos + seqlen, :] = values_store
+ self.k[:batch_size, :, :, start_pos : start_pos + seqlen, :] = keys_store
+
+ def roll_kv(self, roll_len, start_pos):
+ # Roll only the necessary part of the cache to the left
+ self.v[:, :, :-roll_len, :] = self.v[:, :, roll_len:, :]
+ self.k[:, :, :, :-roll_len, :] = self.k[:, :, :, roll_len:, :]
+
+ # Zero out the new part
+ self.v[:, :, -roll_len:, :] = 0
+ self.k[:, :, :, -roll_len:, :] = 0
+
+ return start_pos - roll_len
+
+ def to(self, device):
+ self.k = self.k.to(device)
+ self.v = self.v.to(device)
+