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# Licensed under Apache-2.0; see LICENSE.
from __future__ import annotations
import time
import torch
from .protocol import ABC_END, CODEC_OFFSET, CODEC_SIZE, CONTEXT, EOD, MUSIC_END
class YuE2StaticKVCache:
"""Preallocated token cache for eager decoding; returns views of the filled prefix without copying history."""
def __init__(self, num_layers, batch_size, num_kv_heads, max_seq_len, head_dim, dtype, device):
self.num_layers = num_layers
self.max_seq_len = max_seq_len
self.seen_tokens = 0
shape = (batch_size, num_kv_heads, max_seq_len, head_dim)
self.keys = [torch.zeros(shape, dtype=dtype, device=device) for _ in range(num_layers)]
self.values = [torch.zeros(shape, dtype=dtype, device=device) for _ in range(num_layers)]
def get_seq_length(self):
return self.seen_tokens
def update(self, key, value, layer_idx):
start, end = self.seen_tokens, self.seen_tokens + key.shape[1]
if end > self.max_seq_len:
raise ValueError(f"KV cache capacity {self.max_seq_len} exceeded by {end}; generation was not shortened")
self.keys[layer_idx][:, :, start:end] = key.transpose(1, 2)
self.values[layer_idx][:, :, start:end] = value.transpose(1, 2)
if layer_idx == self.num_layers - 1:
self.seen_tokens = end
return self.keys[layer_idx][:, :, :end].transpose(1, 2), self.values[layer_idx][:, :, :end].transpose(1, 2)
def synchronize(device):
if device.type == "cuda":
torch.cuda.synchronize(device)
elif device.type == "mps":
torch.mps.synchronize()
def window_penalty(logits, recent_ids, penalty):
if penalty == 1.0 or len(recent_ids) == 0:
return logits
recent = torch.as_tensor(recent_ids, dtype=torch.long, device=logits.device).reshape(1, -1)
freq = torch.zeros_like(logits)
freq.scatter_add_(-1, recent, torch.ones_like(recent, dtype=logits.dtype))
alpha = penalty**freq
return torch.where(logits < 0, logits * alpha, logits / alpha)
def distribution(logits, sampling, history, step, phase, legacy_off=False):
# Planning-off requests keep the release's BF16 logits and top-3 floor; other modes sample from FP32 logits.
scores = logits.clone() if legacy_off else logits.float().clone()
end = ABC_END if phase == "abc" else MUSIC_END
allowed = torch.full_like(scores, float("-inf"))
if phase == "abc":
allowed[..., :EOD] = 0
else:
allowed[..., CODEC_OFFSET : CODEC_OFFSET + CODEC_SIZE] = 0
allowed[..., end] = 0
scores = scores + allowed
if step < sampling.min_tokens:
scores[..., end] = -torch.inf
scores = window_penalty(scores, history[-sampling.penalty_window :], sampling.repetition_penalty)
if sampling.temperature == 0:
return scores
if sampling.temperature != 1:
scores = scores / sampling.temperature
threshold = scores.topk(min(sampling.top_k, scores.shape[-1])).values[..., -1, None]
scores = scores.masked_fill(scores < threshold, -torch.inf)
if sampling.top_p < 1:
values, indices = scores.sort(descending=True)
probabilities = values.softmax(-1)
removed = probabilities.cumsum(-1) - probabilities > sampling.top_p
removed[..., : 3 if legacy_off else 1] = False
values = values.masked_fill(removed, -torch.inf)
scores = values.scatter(-1, indices, values)
return scores
@torch.inference_mode()
def generate_tokens(
transformer,
prefix,
sampling,
seed,
phase,
device,
negative=None,
combine_logits=None,
legacy_off=False,
cancelled=None,
on_token=None,
graph_decoder=None,
):
"""Sample one stage's tokens.
With `negative`, `combine_logits(conditional, unconditional)` applies guidance. `graph_decoder` (the `GraphAR`
class) decodes with CUDA graphs on CUDA devices; elsewhere decoding stays eager.
"""
if len(prefix) + sampling.max_tokens > CONTEXT:
raise ValueError("Prefix + requested generation budget exceeds 24576; no implicit truncation")
if negative is not None and (combine_logits is None or len(negative) + sampling.max_tokens > CONTEXT):
raise ValueError("Guidance needs `combine_logits` and a negative prefix that fits the context")
if cancelled is not None and cancelled():
raise InterruptedError("Cancelled before prefill")
# Both stages reset the request seed, as the release does.
rng_device = device if device.type in {"cpu", "cuda"} else torch.device("cpu")
generator = torch.Generator(device=rng_device).manual_seed(seed)
config = transformer.config
def prefill(ids):
cache = YuE2StaticKVCache(
num_layers=config.num_layers,
batch_size=1,
num_kv_heads=config.num_key_value_heads,
max_seq_len=len(ids) + sampling.max_tokens,
head_dim=config.attention_head_dim,
dtype=transformer.dtype,
device=device,
)
logits = transformer(torch.tensor([ids], device=device), kv_cache=cache, logits_to_keep=1).logits
return logits[:, -1, :], cache
graph = None
positive_cache = negative_cache = None
synchronize(device)
start = time.perf_counter()
try:
if graph_decoder is not None and device.type == "cuda":
graph = graph_decoder(
transformer, [prefix] if negative is None else [prefix, negative], sampling.max_tokens, device
)
logits = graph.prefill()
conditional = logits[:1]
unconditional = logits[1:] if negative is not None else None
else:
conditional, positive_cache = prefill(prefix)
unconditional = None
if negative is not None:
unconditional, negative_cache = prefill(negative)
synchronize(device)
prefill_seconds = time.perf_counter() - start
history, first, eos = [], None, False
end = ABC_END if phase == "abc" else MUSIC_END
for step in range(sampling.max_tokens):
if cancelled is not None and cancelled():
raise InterruptedError(f"Cancelled during {phase}")
logits = conditional if negative is None else combine_logits(conditional, unconditional)
scores = distribution(logits, sampling, history, step, phase, legacy_off)
if sampling.temperature == 0:
next_id = scores.argmax(-1, keepdim=True)
else:
probabilities = scores.softmax(-1)
if device.type == "mps":
next_id = torch.multinomial(probabilities.cpu(), 1, generator=generator).to(device)
else:
next_id = torch.multinomial(probabilities, 1, generator=generator)
token = int(next_id.item())
if first is None:
first = time.perf_counter() - start
if on_token is not None:
on_token(phase, token)
if token == end:
eos = True
break
history.append(token)
if step + 1 < sampling.max_tokens:
if graph is not None:
branch_logits = graph.step(next_id)
conditional = branch_logits[:1]
unconditional = branch_logits[1:] if negative is not None else None
else:
conditional = transformer(next_id, kv_cache=positive_cache, logits_to_keep=1).logits[:, -1, :]
if negative_cache is not None:
unconditional = transformer(next_id, kv_cache=negative_cache, logits_to_keep=1).logits[
:, -1, :
]
synchronize(device)
seconds = time.perf_counter() - start
count = len(history) + int(eos)
timing = {
"seconds": seconds,
"prefill_seconds": prefill_seconds,
"ttft_seconds": first,
"output_tokens": count,
"content_tokens": len(history),
"output_tps": count / seconds,
"prefix_tokens": len(prefix),
"cfg_branches": 1 if negative is None else 2,
"execution": "cuda_graph" if graph is not None else "eager",
}
return history, timing, not eos
finally:
if graph is not None:
graph.close()
positive_cache = negative_cache = None
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