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Sep 4, 2025
Merged

Static attention batch size > 1 #13919

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37 changes: 24 additions & 13 deletions examples/models/llama/static_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -242,7 +242,8 @@ def __init__(
config: ModelArgs,
input_len: int,
cache_lens: Union[int, List[int]],
dtype=torch.float32,
batch_size: int = 1,
dtype: torch.dtype = torch.float32,
style: str = "shift_pointer",
mask_val: float = float("-inf"),
):
Expand All @@ -266,15 +267,21 @@ def __init__(
if split_mha:
self.k_caches = {
StaticKVCache.calculate_cache_key(layer_id, head_id): torch.zeros(
1, cache_lens[layer_id], none_throws(config.head_dim), dtype=dtype
batch_size,
cache_lens[layer_id],
none_throws(config.head_dim),
dtype=dtype,
)
for layer_id in range(config.n_layers)
for head_id in range(none_throws(config.n_kv_heads))
if cache_lens[layer_id] > 0
}
self.v_caches = {
StaticKVCache.calculate_cache_key(layer_id, head_id): torch.zeros(
1, cache_lens[layer_id], none_throws(config.head_dim), dtype=dtype
batch_size,
cache_lens[layer_id],
none_throws(config.head_dim),
dtype=dtype,
)
for layer_id in range(config.n_layers)
for head_id in range(none_throws(config.n_kv_heads))
Expand All @@ -283,7 +290,7 @@ def __init__(
else:
self.k_caches = {
StaticKVCache.calculate_cache_key(layer_id, 0): torch.zeros(
1,
batch_size,
none_throws(config.n_kv_heads),
cache_lens[layer_id],
none_throws(config.head_dim),
Expand All @@ -293,7 +300,7 @@ def __init__(
}
self.v_caches = {
StaticKVCache.calculate_cache_key(layer_id, 0): torch.zeros(
1,
batch_size,
none_throws(config.n_kv_heads),
cache_lens[layer_id],
none_throws(config.head_dim),
Expand Down Expand Up @@ -323,7 +330,7 @@ def reset(self):
def prefill(
self,
model: Callable[..., Any],
tokens: List[int],
tokens: Union[List[int], torch.Tensor],
) -> torch.Tensor:
if self.cache_full:
raise RuntimeError("KV cache is full.")
Expand All @@ -336,18 +343,21 @@ def prefill(
)
)

if isinstance(tokens, list):
tokens = torch.tensor([tokens], dtype=torch.int32)

logits = None
all_logits = None
for i in range(0, len(tokens), self.input_len):
logits = self._run_once(model, tokens[i : i + self.input_len])[0]
for i in range(0, tokens.size(1), self.input_len):
logits = self._run_once(model, tokens[:, i : i + self.input_len])[0]
if self.config.generate_full_logits:
if all_logits is None:
all_logits = logits
else:
all_logits = torch.cat([all_logits, logits], dim=1)

if self.config.generate_full_logits:
return all_logits[:, : len(tokens), :]
return all_logits[:, : tokens.size(1), :]

return logits

Expand Down Expand Up @@ -510,15 +520,16 @@ def lookahead_decode( # noqa: C901
def _run_once(
self,
model: Callable[..., Any],
tokens: List[int],
tokens: Union[List[int], torch.Tensor],
non_padded_len: Optional[int] = None,
freqs_cos_override: Optional[torch.Tensor] = None,
freqs_sin_override: Optional[torch.Tensor] = None,
):
n_tokens = len(tokens)
if isinstance(tokens, list):
tokens = torch.tensor([tokens], dtype=torch.int32)
n_tokens = tokens.size(1)
if n_tokens < self.input_len:
tokens += [0] * (self.input_len - n_tokens)
tokens = torch.tensor([tokens], dtype=torch.int32) # pyre-ignore[9]
tokens = F.pad(tokens, (0, self.input_len - n_tokens))
if freqs_cos_override is None:
freqs_cos_override = self.freqs_cos[self.pos : self.pos + self.input_len]
if freqs_sin_override is None:
Expand Down
67 changes: 63 additions & 4 deletions examples/models/llama/tests/test_static_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def test_with_style(style):
test_with_style("shift_pointer")
test_with_style("smart_mask")

def _get_test_transformers(self, config, attention_type="static"):
def _get_test_transformers(self, config, attention_type="static", use_conv2d=False):
mha_transformer = construct_transformer(config).eval()

config = copy.copy(config)
Expand All @@ -207,6 +207,8 @@ def _get_test_transformers(self, config, attention_type="static"):
):
static_layer.attention.load_weights_from_attention_mha(mha_layer.attention)
static_layer.attention.adopt_hf_rope()
if use_conv2d:
static_layer.linear_to_conv2d()
config.use_hf_rope = True

return mha_transformer, static_transformer, config
Expand All @@ -220,7 +222,8 @@ def test_within_transformer(self):
n_layers=4,
vocab_size=128,
)
x = torch.randint(config.vocab_size, (1, config.max_seq_len))
batch_size = 3
x = torch.randint(config.vocab_size, (batch_size, config.max_seq_len))
n_chunks = 3
chunk_len = config.max_seq_len // n_chunks
cache_len = config.max_seq_len - chunk_len
Expand All @@ -235,13 +238,13 @@ def test(style, attention_type):
expected = mha_transformer(x)

mgr = StaticAttentionIOManager(
static_config, chunk_len, cache_len, style=style
static_config, chunk_len, cache_len, style=style, batch_size=batch_size
)
ys = []
for i in range(n_chunks):
y_i = mgr.prefill(
static_transformer,
x[0][i * chunk_len : (i + 1) * chunk_len].tolist(),
x[:, i * chunk_len : (i + 1) * chunk_len],
)
ys.append(y_i)

Expand Down Expand Up @@ -300,3 +303,59 @@ def test_lookahead_decode(self):
ngram_caches=ngram_caches,
)
self.assertEqual(lookahead_output[: len(ref_output)], ref_output)

def test_batched_export_with_backprop(self):
config = ModelArgs(
dim=64,
n_heads=4,
n_kv_heads=2,
max_seq_len=128,
n_layers=4,
vocab_size=128,
generate_full_logits=True,
)
_, static_transformer, static_config = self._get_test_transformers(config)
batch_size = 4
input_len = 32
cache_len = static_config.max_seq_len - input_len
mgr = StaticAttentionIOManager(
static_config, input_len, cache_len, batch_size=batch_size
)
example_inputs = (
torch.zeros(batch_size, input_len),
{
"masks": mgr.masks,
"freqs_cos_override": mgr.freqs_cos[:input_len],
"freqs_sin_override": mgr.freqs_sin[:input_len],
"in_cache_state": (mgr.k_caches, mgr.v_caches),
},
)
batched_gm = torch.export.export(static_transformer, example_inputs).module()

# Test backprop
for _ in range(10):
x = torch.randint(config.vocab_size, (batch_size, input_len))
y = mgr.prefill(batched_gm, x)
loss = torch.nn.functional.cross_entropy(
y, torch.rand(batch_size, input_len, config.vocab_size)
)
loss.backward()
mgr.reset()

# Test loading state dict into a non batched graph for inference
mgr = StaticAttentionIOManager(
static_config, input_len, cache_len, batch_size=1
)
example_inputs = (
torch.zeros(1, input_len),
{
"masks": mgr.masks,
"freqs_cos_override": mgr.freqs_cos[:input_len],
"freqs_sin_override": mgr.freqs_sin[:input_len],
"in_cache_state": (mgr.k_caches, mgr.v_caches),
},
)
non_batched_gm = torch.export.export(
static_transformer, example_inputs
).module()
non_batched_gm.load_state_dict(batched_gm.state_dict())
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