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from dataclasses import dataclass
from typing import Any, Iterable, Optional, Sequence, Tuple
import torch
from torch.nn import functional as F
from transformers.cache_utils import Cache
from transformers.configuration_utils import PretrainedConfig
@dataclass
class _ActionFlowInputs:
trajectory: torch.Tensor
context: Any
modulations: Sequence[Any]
action_dim_is_pad: Optional[torch.Tensor]
@dataclass
class _ActionFlowCudaGraph:
key: Tuple[Any, ...]
graph: torch.cuda.CUDAGraph
static_inputs: _ActionFlowInputs
output: torch.Tensor
@dataclass
class _DepthDecodeCudaGraphLayerStage:
residual: torch.Tensor
query: torch.Tensor
key: torch.Tensor
value: torch.Tensor
@dataclass
class _DepthDecodeCudaGraphPostStage:
graph: torch.cuda.CUDAGraph
attn_context: torch.Tensor
@dataclass
class _DepthDecodeCudaGraph:
cache_key: Tuple[Any, ...]
pre_graph: torch.cuda.CUDAGraph
token_ids: torch.Tensor
cos: torch.Tensor
sin: torch.Tensor
positions: torch.Tensor
stages: Sequence[_DepthDecodeCudaGraphLayerStage]
post_graphs: Sequence[_DepthDecodeCudaGraphPostStage]
output: torch.Tensor
@dataclass
class _DepthDecodeCudaGraphSpec:
eligible: bool
cache_key_prefix: Tuple[Any, ...]
num_hidden_layers: int
head_dim: int
num_attention_heads: int
def _cache_seq_len_int(past_key_values: Optional[Cache]) -> int:
if past_key_values is None:
return 0
seq_len = past_key_values.get_seq_length()
if torch.is_tensor(seq_len):
return int(seq_len.item())
return int(seq_len)
def _cache_max_len_int(past_key_values: Optional[Cache]) -> int:
if past_key_values is None:
return -1
max_len = past_key_values.get_max_cache_shape()
if torch.is_tensor(max_len):
return int(max_len.item())
return int(max_len)
def _iter_cache_key_values(
past_key_values: Cache,
) -> Iterable[Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]]:
layers = getattr(past_key_values, "layers", None)
if layers is not None:
for layer in layers:
yield getattr(layer, "keys", None), getattr(layer, "values", None)
return
for layer in past_key_values:
yield layer[0], layer[1]
class _DepthDecodeStaticLayerCache:
is_compileable = False
is_sliding = False
def __init__(self, max_cache_len: int) -> None:
self.max_cache_len = int(max_cache_len)
self.cumulative_length = 0
self.keys: Optional[torch.Tensor] = None
self.values: Optional[torch.Tensor] = None
def _allocate(self, key_states: torch.Tensor, value_states: torch.Tensor) -> None:
bsz, n_heads = key_states.shape[:2]
self.keys = torch.empty(
(bsz, n_heads, self.max_cache_len, key_states.shape[-1]),
dtype=key_states.dtype,
device=key_states.device,
)
self.values = torch.empty(
(bsz, n_heads, self.max_cache_len, value_states.shape[-1]),
dtype=value_states.dtype,
device=value_states.device,
)
def update(
self,
key_states: torch.Tensor,
value_states: torch.Tensor,
*args,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
if self.keys is None:
self._allocate(key_states, value_states)
start = self.cumulative_length
end = start + key_states.shape[-2]
if end > self.max_cache_len:
raise RuntimeError(
f"KV cache length {end} exceeds max_cache_len={self.max_cache_len}."
)
self.keys[:, :, start:end, :].copy_(key_states)
self.values[:, :, start:end, :].copy_(value_states)
self.cumulative_length = end
return self.keys[:, :, :end, :], self.values[:, :, :end, :]
def get_seq_length(self) -> int:
return self.cumulative_length
def get_max_cache_shape(self) -> int:
return -1
def reset(self) -> None:
self.cumulative_length = 0
class _DepthDecodeStaticCache(Cache):
def __init__(self, config: PretrainedConfig, max_cache_len: int) -> None:
text_config = config.get_text_config(decoder=True)
super().__init__(
layers=[
_DepthDecodeStaticLayerCache(max_cache_len=max_cache_len)
for _ in range(text_config.num_hidden_layers)
]
)
def get_seq_length(self, layer_idx: int = 0) -> int:
return self.layers[layer_idx].get_seq_length()
def get_max_cache_shape(self, layer_idx: int = 0) -> int:
return self.layers[layer_idx].get_max_cache_shape()
def reset(self) -> None:
for layer in self.layers:
layer.reset()
class ActionCudaGraphManager:
def __init__(self, model: Any) -> None:
self.model = model
self.enabled = True
self.action_flow_graph: Optional[_ActionFlowCudaGraph] = None
def set_enabled(self, enabled: bool) -> None:
self.enabled = bool(enabled)
def can_use_action_flow(self, inputs: _ActionFlowInputs) -> bool:
action_model = self.model
if not self.enabled:
return False
if action_model.training or action_model._require_action_expert().training:
return False
if inputs.trajectory.device.type != "cuda":
return False
def all_on_cuda():
yield inputs.trajectory
for k, v in inputs.context.kv_contexts:
yield k
yield v
for t in (
inputs.context.cross_mask,
inputs.context.self_mask,
inputs.context.valid_action,
inputs.action_dim_is_pad,
):
if t is not None:
yield t
if inputs.context.rope_cache is not None:
yield from inputs.context.rope_cache
for step in inputs.modulations:
yield step.conditioning
for block_modulation in step.block_modulations:
yield from block_modulation
yield from step.final_modulation
return all(t.device.type == "cuda" for t in all_on_cuda())
def run_action_flow(
self,
inputs: _ActionFlowInputs,
steps: int,
run_loop,
) -> torch.Tensor:
key = _cuda_graph_key(inputs, steps)
cache = self.action_flow_graph
if cache is None or cache.key != key:
static_inputs = _clone_static_inputs(inputs)
graph, output = _capture_cuda_graph(
lambda: run_loop(static_inputs, steps),
inputs.trajectory.device,
after_warmup=lambda: static_inputs.trajectory.copy_(inputs.trajectory),
)
cache = _ActionFlowCudaGraph(
key=key,
graph=graph,
static_inputs=static_inputs,
output=output,
)
self.action_flow_graph = cache
else:
_copy_inputs_(cache.static_inputs, inputs)
cache.graph.replay()
return cache.output.clone()
class DepthDecodeCudaGraphManager:
def __init__(self, model: Any) -> None:
self.model = model
self.backbone = model.model
self.enabled = True
self.graph: Optional[_DepthDecodeCudaGraph] = None
self.graph_spec: Optional[_DepthDecodeCudaGraphSpec] = None
def set_enabled(self, enabled: bool) -> None:
self.enabled = bool(enabled)
def make_static_cache(self, max_cache_len: int) -> _DepthDecodeStaticCache:
return _DepthDecodeStaticCache(
config=self.model.config.text_config,
max_cache_len=max_cache_len,
)
def _depth_decode_spec(self) -> _DepthDecodeCudaGraphSpec:
static = self.graph_spec
if static is None:
cfg = self.backbone.transformer.config
rotary_emb = getattr(self.backbone.transformer, "rotary_emb", None)
static = _DepthDecodeCudaGraphSpec(
eligible=(
not cfg.norm_after
and cfg.rope_scaling_layers is None
and getattr(rotary_emb, "rope_type", None) == "default"
and cfg._attn_implementation == "sdpa"
),
cache_key_prefix=(
cfg.hidden_size,
cfg.num_attention_heads,
cfg.num_key_value_heads,
cfg.head_dim,
cfg.num_hidden_layers,
cfg.use_qk_norm,
cfg.qk_norm_type,
cfg._attn_implementation,
),
num_hidden_layers=cfg.num_hidden_layers,
head_dim=cfg.head_dim,
num_attention_heads=cfg.num_attention_heads,
)
self.graph_spec = static
return static
def can_use(
self,
next_input_ids: torch.Tensor,
*,
past_key_values: Cache,
attention_bias: torch.Tensor,
) -> bool:
if (
not self.enabled
or self.model.training
or self.backbone.transformer.training
):
return False
if next_input_ids.device.type != "cuda":
return False
if (
next_input_ids.ndim != 2
or next_input_ids.shape[0] != 1
or next_input_ids.shape[1] != 1
):
return False
if not isinstance(past_key_values, _DepthDecodeStaticCache):
return False
if (
not torch.is_tensor(attention_bias)
or attention_bias.device != next_input_ids.device
):
return False
return self._depth_decode_spec().eligible
def _depth_decode_key(
self,
next_input_ids: torch.Tensor,
attention_bias: torch.Tensor,
) -> Tuple[Any, ...]:
device = next_input_ids.device
return (
self._depth_decode_spec().cache_key_prefix,
device.type,
device.index,
self.model.lm_head.weight.dtype,
attention_bias.shape[-1],
)
def _select_depth_decode_rope(
self, cos: torch.Tensor, sin: torch.Tensor, *, past_length: int
) -> None:
emb = self.backbone.transformer.rotary_emb
cos.copy_(emb._pos_cos_cache[0, :, past_length : past_length + 1, :])
sin.copy_(emb._pos_sin_cache[0, :, past_length : past_length + 1, :])
def _depth_decode_pre_layer(
self,
layer_idx: int,
hidden_states: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
block = self.backbone.transformer.blocks[layer_idx]
attention = block.self_attn
residual = hidden_states
hidden_states = block.attn_norm(hidden_states)
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, attention.head_dim)
qkv = attention.att_proj(hidden_states)
query_states, key_states, value_states = qkv.split(attention.fused_dims, dim=-1)
value_states = value_states.view(hidden_shape)
apply_qk_norm = attention.q_norm is not None and attention.k_norm is not None
norm_after_view = apply_qk_norm and attention.qk_norm_type == "qwen3"
if apply_qk_norm and not norm_after_view:
query_states = attention.q_norm(query_states)
key_states = attention.k_norm(key_states)
query_states = query_states.view(hidden_shape)
key_states = key_states.view(hidden_shape)
if norm_after_view:
query_states = attention.q_norm(query_states)
key_states = attention.k_norm(key_states)
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
query_states, key_states = _apply_rotary_pos_emb(
query_states, key_states, cos, sin
)
return residual, query_states, key_states, value_states
def _depth_decode_pre0(
self,
token_ids: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
inputs_embeds = self.model._embed_base_tokens(token_ids)
return self._depth_decode_pre_layer(0, inputs_embeds, cos, sin)
def _depth_decode_post_layer(
self,
layer_idx: int,
residual: torch.Tensor,
attn_context: torch.Tensor,
) -> torch.Tensor:
block = self.backbone.transformer.blocks[layer_idx]
attention = block.self_attn
input_shape = residual.shape[:-1]
attn_output = attn_context.reshape(*input_shape, -1).contiguous()
attn_output = attention.attn_out(attn_output)
hidden_states = residual + block.dropout(attn_output)
residual = hidden_states
hidden_states = block.ff_norm(hidden_states)
hidden_states = block.mlp(hidden_states)
hidden_states = residual + block.dropout(hidden_states)
return hidden_states
def _depth_decode_post_and_pre_next(
self,
layer_idx: int,
residual: torch.Tensor,
attn_context: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
hidden_states = self._depth_decode_post_layer(layer_idx, residual, attn_context)
return self._depth_decode_pre_layer(layer_idx + 1, hidden_states, cos, sin)
def _depth_decode_last_post(
self,
layer_idx: int,
residual: torch.Tensor,
attn_context: torch.Tensor,
) -> torch.Tensor:
hidden_states = self._depth_decode_post_layer(layer_idx, residual, attn_context)
return self.backbone.transformer.ln_f(hidden_states)
def _build_depth_decode_graph(
self,
next_input_ids: torch.Tensor,
*,
past_length: int,
attention_bias: torch.Tensor,
) -> _DepthDecodeCudaGraph:
text_config = self.backbone.transformer.config
device = next_input_ids.device
dtype = self.model.lm_head.weight.dtype
static = self._depth_decode_spec()
num_layers = static.num_hidden_layers
head_dim = static.head_dim
max_cache_len = int(attention_bias.shape[-1])
max_rope_len = max(int(text_config.max_position_embeddings or 0), max_cache_len)
self.backbone.transformer.prepare_rope_cache(
device=device, max_seq_len=max_rope_len
)
token_ids = torch.empty((1, 1), device=device, dtype=torch.long)
cos = torch.empty((1, 1, head_dim), device=device, dtype=dtype)
sin = torch.empty_like(cos)
positions = torch.arange(max_cache_len, device=device, dtype=torch.long)
context_shape = (1, 1, static.num_attention_heads, head_dim)
token_ids.copy_(next_input_ids)
self._select_depth_decode_rope(cos, sin, past_length=past_length)
pre_graph, pre_output = _capture_cuda_graph(
lambda: self._depth_decode_pre0(token_ids, cos, sin),
device,
)
stages = [_DepthDecodeCudaGraphLayerStage(*pre_output)]
post_graphs = []
for layer_idx in range(num_layers - 1):
stage = stages[-1]
attn_context = torch.empty(context_shape, device=device, dtype=dtype)
graph, output = _capture_cuda_graph(
lambda layer_idx=layer_idx, stage=stage, attn_context=attn_context: (
self._depth_decode_post_and_pre_next(
layer_idx,
stage.residual,
attn_context,
cos,
sin,
)
),
device,
)
post_graphs.append(
_DepthDecodeCudaGraphPostStage(graph=graph, attn_context=attn_context)
)
stages.append(_DepthDecodeCudaGraphLayerStage(*output))
last_stage = stages[-1]
last_attn_context = torch.empty(context_shape, device=device, dtype=dtype)
last_graph, last_output = _capture_cuda_graph(
lambda: self._depth_decode_last_post(
num_layers - 1,
last_stage.residual,
last_attn_context,
),
device,
)
post_graphs.append(
_DepthDecodeCudaGraphPostStage(
graph=last_graph, attn_context=last_attn_context
)
)
return _DepthDecodeCudaGraph(
cache_key=self._depth_decode_key(next_input_ids, attention_bias),
pre_graph=pre_graph,
token_ids=token_ids,
cos=cos,
sin=sin,
positions=positions,
stages=tuple(stages),
post_graphs=tuple(post_graphs),
output=last_output,
)
def _get_depth_decode_graph(
self,
next_input_ids: torch.Tensor,
*,
past_length: int,
attention_bias: torch.Tensor,
) -> _DepthDecodeCudaGraph:
key = self._depth_decode_key(next_input_ids, attention_bias)
decode_graph = self.graph
if decode_graph is None or decode_graph.cache_key != key:
decode_graph = self._build_depth_decode_graph(
next_input_ids,
past_length=past_length,
attention_bias=attention_bias,
)
self.graph = decode_graph
else:
decode_graph.token_ids.copy_(next_input_ids)
self._select_depth_decode_rope(
decode_graph.cos, decode_graph.sin, past_length=past_length
)
return decode_graph
def _run_depth_decode_attention_core(
self,
layer_idx: int,
stage: _DepthDecodeCudaGraphLayerStage,
*,
past_key_values: Cache,
attention_bias: torch.Tensor,
cache_position: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> torch.Tensor:
attention = self.backbone.transformer.blocks[layer_idx].self_attn
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_values.update(
stage.key,
stage.value,
layer_idx,
cache_kwargs,
)
key_states = _repeat_kv(key_states, attention.num_key_value_groups)
value_states = _repeat_kv(value_states, attention.num_key_value_groups)
attn_output = F.scaled_dot_product_attention(
stage.query,
key_states,
value_states,
attn_mask=attention_bias,
dropout_p=0.0,
is_causal=False,
)
return attn_output.transpose(1, 2)
def run(
self,
next_input_ids: torch.Tensor,
*,
past_key_values: Cache,
attention_bias: torch.Tensor,
past_length: int,
) -> Tuple[torch.Tensor, Cache]:
end = past_length + 1
decode_graph = self._get_depth_decode_graph(
next_input_ids,
past_length=past_length,
attention_bias=attention_bias,
)
cache_position = decode_graph.positions[past_length:end]
attention_bias_q = attention_bias[:, :, past_length:end, :end]
decode_graph.pre_graph.replay()
for layer_idx, post_graph in enumerate(decode_graph.post_graphs):
attn_context = self._run_depth_decode_attention_core(
layer_idx,
decode_graph.stages[layer_idx],
past_key_values=past_key_values,
attention_bias=attention_bias_q,
cache_position=cache_position,
cos=decode_graph.cos,
sin=decode_graph.sin,
)
post_graph.attn_context.copy_(attn_context)
post_graph.graph.replay()
return decode_graph.output, past_key_values
def _cuda_graph_tensor_signature(
tensor: Optional[torch.Tensor],
) -> Optional[Tuple[Any, ...]]:
if tensor is None:
return None
return (
tuple(tensor.shape),
tuple(tensor.stride()),
str(tensor.dtype),
str(tensor.device),
)
def _cuda_graph_context_signature(context: Any) -> Tuple[Any, ...]:
sig = _cuda_graph_tensor_signature
return (
tuple((sig(k), sig(v)) for k, v in context.kv_contexts),
sig(context.cross_mask),
sig(context.self_mask),
sig(context.valid_action),
None
if context.rope_cache is None
else tuple(sig(t) for t in context.rope_cache),
)
def _cuda_graph_modulation_signature(modulations: Sequence[Any]) -> Tuple[Any, ...]:
sig = _cuda_graph_tensor_signature
return tuple(
(
sig(step.conditioning),
tuple(
tuple(sig(t) for t in block_modulation)
for block_modulation in step.block_modulations
),
tuple(sig(t) for t in step.final_modulation),
)
for step in modulations
)
def _cuda_graph_key(inputs: _ActionFlowInputs, steps: int) -> Tuple[Any, ...]:
sig = _cuda_graph_tensor_signature
return (
sig(inputs.trajectory),
_cuda_graph_context_signature(inputs.context),
_cuda_graph_modulation_signature(inputs.modulations),
sig(inputs.action_dim_is_pad),
int(steps),
)
def _clone_static_tensor(tensor: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
if tensor is None:
return None
static = torch.empty_strided(
tuple(tensor.shape),
tuple(tensor.stride()),
device=tensor.device,
dtype=tensor.dtype,
)
static.copy_(tensor)
return static
def _clone_static_context(context: Any) -> Any:
rope_cache = None
if context.rope_cache is not None:
rope_cache = tuple(_clone_static_tensor(t) for t in context.rope_cache)
return context.__class__(
kv_contexts=tuple(
(_clone_static_tensor(k), _clone_static_tensor(v))
for k, v in context.kv_contexts
),
cross_mask=_clone_static_tensor(context.cross_mask),
self_mask=_clone_static_tensor(context.self_mask),
valid_action=_clone_static_tensor(context.valid_action),
rope_cache=rope_cache,
)
def _clone_static_modulations(modulations: Sequence[Any]) -> Sequence[Any]:
return tuple(
step.__class__(
conditioning=_clone_static_tensor(step.conditioning),
block_modulations=tuple(
tuple(_clone_static_tensor(t) for t in block_modulation)
for block_modulation in step.block_modulations
),
final_modulation=tuple(
_clone_static_tensor(t) for t in step.final_modulation
),
)
for step in modulations
)
def _clone_static_inputs(inputs: _ActionFlowInputs) -> _ActionFlowInputs:
return _ActionFlowInputs(
trajectory=_clone_static_tensor(inputs.trajectory),
context=_clone_static_context(inputs.context),
modulations=_clone_static_modulations(inputs.modulations),
action_dim_is_pad=_clone_static_tensor(inputs.action_dim_is_pad),
)
def _copy_context_(dst: Any, src: Any) -> None:
for (dst_k, dst_v), (src_k, src_v) in zip(dst.kv_contexts, src.kv_contexts):
dst_k.copy_(src_k)
dst_v.copy_(src_v)
if src.cross_mask is not None:
dst.cross_mask.copy_(src.cross_mask)
if src.self_mask is not None:
dst.self_mask.copy_(src.self_mask)
if src.valid_action is not None:
dst.valid_action.copy_(src.valid_action)
if src.rope_cache is not None:
for dst_tensor, src_tensor in zip(dst.rope_cache, src.rope_cache):
dst_tensor.copy_(src_tensor)
def _copy_inputs_(dst: _ActionFlowInputs, src: _ActionFlowInputs) -> None:
dst.trajectory.copy_(src.trajectory)
_copy_context_(dst.context, src.context)
if src.action_dim_is_pad is not None:
dst.action_dim_is_pad.copy_(src.action_dim_is_pad)
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def _apply_rotary_pos_emb(
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
unsqueeze_dim: int = 1,
) -> Tuple[torch.Tensor, torch.Tensor]:
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (_rotate_half(q) * sin)
k_embed = (k * cos) + (_rotate_half(k) * sin)
return q_embed, k_embed
def _repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(
batch, num_key_value_heads, n_rep, slen, head_dim
)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def _capture_cuda_graph(
fn,
device: torch.device,
*,
after_warmup=None,
) -> Tuple[torch.cuda.CUDAGraph, Any]:
warmup_stream = torch.cuda.Stream(device=device)
warmup_stream.wait_stream(torch.cuda.current_stream(device))
with torch.cuda.stream(warmup_stream):
fn()
torch.cuda.current_stream(device).wait_stream(warmup_stream)
if after_warmup is not None:
after_warmup()
graph = torch.cuda.CUDAGraph()
with torch.cuda.graph(graph):
output = fn()
return graph, output
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