Integrate Flex Decoding

generate.py

@@ -12,6 +12,7 @@
 import torch
 import torch._dynamo.config
 import torch._inductor.config
+from torch.nn.attention.flex_attention import BlockMask, create_block_mask
 
 def device_sync(device):
     if "cuda" in device:
@@ -56,29 +57,39 @@ def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
     idx_next = multinomial_sample_one_no_sync(probs)
     return idx_next, probs
 
+def roundup(val, multiplier):
+    return ((val - 1) // multiplier + 1) * multiplier
+
+def causal_mask(b, h, q, kv):
+    return q >= kv
+
 def prefill(model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs) -> torch.Tensor:
     # input_pos: [B, S]
-    logits = model(x, input_pos)
+    mask = create_block_mask(causal_mask, 1, 1, input_pos.shape[0], model.max_seq_length, device="cuda")
+    logits = model(mask, x, input_pos)
     return sample(logits, **sampling_kwargs)[0]
 
-def decode_one_token(model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
+def decode_one_token(model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, block_mask: BlockMask, **sampling_kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
     # input_pos: [B, 1]
     assert input_pos.shape[-1] == 1
-    logits = model(x, input_pos)
+    block_index = input_pos // block_mask.BLOCK_SIZE[0]
+    mask = block_mask[:, :, block_index]
+    mask.mask_mod = block_mask.mask_mod
+    logits = model(mask, x, input_pos)
     return sample(logits, **sampling_kwargs)
 
 def decode_n_tokens(model: Transformer, cur_token: torch.Tensor, input_pos: torch.Tensor, num_new_tokens: int, callback=lambda _: _, **sampling_kwargs):
+    block_mask = create_block_mask(causal_mask, 1, 1, model.max_seq_length, model.max_seq_length, device="cuda")
     new_tokens, new_probs = [], []
     for i in range(num_new_tokens):
-        with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_mem_efficient=False, enable_math=True): # Actually better for Inductor to codegen attention here
-            next_token, next_prob = decode_one_token(
-                model, cur_token, input_pos, **sampling_kwargs
-            )
-            input_pos += 1
-            new_tokens.append(next_token.clone())
-            callback(new_tokens[-1])
-            new_probs.append(next_prob.clone())
-            cur_token = next_token.clone()
+        next_token, next_prob = decode_one_token(
+            model, cur_token, input_pos, block_mask, **sampling_kwargs
+        )
+        input_pos += 1
+        new_tokens.append(next_token.clone())
+        callback(new_tokens[-1])
+        new_probs.append(next_prob.clone())
+        cur_token = next_token.clone()
 
     return new_tokens, new_probs
 

model.py

@@ -10,13 +10,26 @@
 import torch.nn as nn
 from torch import Tensor
 from torch.nn import functional as F
+from torch.nn.attention.flex_attention import (
+    _mask_mod_signature,
+    BlockMask,
+    flex_attention,
+)
 
 
 def find_multiple(n: int, k: int) -> int:
     if n % k == 0:
         return n
     return n + k - (n % k)
 
+
+def get_mask_mod(mask_mod: _mask_mod_signature, offset: int):
+    def _mask_mod(b, h, q, kv):
+        return mask_mod(b, h, q + offset, kv)
+
+    return _mask_mod
+
+
 @dataclass
 class ModelArgs:
     block_size: int = 2048
@@ -102,6 +115,7 @@ def __init__(self, config: ModelArgs) -> None:
         self.mask_cache: Optional[Tensor] = None
         self.max_batch_size = -1
         self.max_seq_length = -1
+        self.get_mask_mod = get_mask_mod
 
     def setup_caches(self, max_batch_size, max_seq_length):
         if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
@@ -120,11 +134,10 @@ def setup_caches(self, max_batch_size, max_seq_length):
             b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim, dtype)
 
         self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base, dtype)
-        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
 
-    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+    def forward(self, mask: BlockMask, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
         assert self.freqs_cis is not None, "Caches must be initialized first"
-        mask = self.causal_mask[None, None, input_pos]
+        mask.mask_mod = self.get_mask_mod(mask.mask_mod, input_pos[0])
         freqs_cis = self.freqs_cis[input_pos]
         x = self.tok_embeddings(idx)
 
@@ -147,7 +160,7 @@ def __init__(self, config: ModelArgs) -> None:
         self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
         self.attention_norm = RMSNorm(config.dim, config.norm_eps)
 
-    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: BlockMask) -> Tensor:
         h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
         out = h + self.feed_forward(self.ffn_norm(h))
         return out
@@ -177,7 +190,7 @@ def load_hook(self, state_dict, prefix, *args):
             wv = state_dict.pop(prefix + "wv.weight")
             state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
 
-    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: BlockMask, input_pos: Optional[Tensor] = None) -> Tensor:
         bsz, seqlen, _ = x.shape
 
         kv_size = self.n_local_heads * self.head_dim
@@ -197,7 +210,7 @@ def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optiona
 
         k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
         v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
-        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+        y = flex_attention(q, k, v, block_mask=mask, enable_gqa=(self.n_head != self.n_local_heads))
 
         y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)