Refactor auto_regressive_inference to reduce memory allocations and cpu-gpy syncs.

Author: AnMakc

model/kronos.py

@@ -388,8 +388,6 @@ def sample_from_logits(logits, temperature=1.0, top_k=None, top_p=None, sample_l
 
 def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max_context, pred_len, clip=5, T=1.0, top_k=0, top_p=0.99, sample_count=5, verbose=False):
  with torch.no_grad():
- batch_size = x.size(0)
- initial_seq_len = x.size(1)
  x = torch.clip(x, -clip, clip)
 
  device = x.device
@@ -398,28 +396,42 @@ def auto_regressive_inference(tokenizer, model, x, x_stamp, y_stamp, max_context
  y_stamp = y_stamp.unsqueeze(1).repeat(1, sample_count, 1, 1).reshape(-1, y_stamp.size(1), y_stamp.size(2)).to(device)
 
  x_token = tokenizer.encode(x, half=True)
+ 
+ initial_seq_len = x.size(1)
+ batch_size = x_token[0].size(0)
+ total_seq_len = initial_seq_len + pred_len
+ full_stamp = torch.cat([x_stamp, y_stamp], dim=1)
 
- def get_dynamic_stamp(x_stamp, y_stamp, current_seq_len, pred_step):
+ generated_pre = x_token[0].new_empty(batch_size, pred_len)
+ generated_post = x_token[1].new_empty(batch_size, pred_len)
 
- if current_seq_len <= max_context - pred_step:
- return torch.cat([x_stamp, y_stamp[:, :pred_step, :]], dim=1)
- else:
- start_idx = max_context - pred_step
- return torch.cat([x_stamp[:, -start_idx:, :], y_stamp[:, :pred_step, :]], dim=1)
+ pre_buffer = x_token[0].new_zeros(batch_size, max_context)
+ post_buffer = x_token[1].new_zeros(batch_size, max_context)
+ buffer_len = min(initial_seq_len, max_context)
+ if buffer_len > 0:
+ start_idx = max(0, initial_seq_len - max_context)
+ pre_buffer[:, :buffer_len] = x_token[0][:, start_idx:start_idx + buffer_len]
+ post_buffer[:, :buffer_len] = x_token[1][:, start_idx:start_idx + buffer_len]
 
  if verbose:
  ran = trange
  else:
  ran = range
  for i in ran(pred_len):
  current_seq_len = initial_seq_len + i
+ window_len = min(current_seq_len, max_context)
 
  if current_seq_len <= max_context:
- input_tokens = x_token
+ input_tokens = [
+ pre_buffer[:, :window_len],
+ post_buffer[:, :window_len]
+ ]
  else:
- input_tokens = [t[:, -max_context:].contiguous() for t in x_token]
+ input_tokens = [pre_buffer, post_buffer]
 
- current_stamp = get_dynamic_stamp(x_stamp, y_stamp, current_seq_len, i)
+ context_end = current_seq_len
+ context_start = max(0, context_end - max_context)
+ current_stamp = full_stamp[:, context_start:context_end, :].contiguous()
 
  s1_logits, context = model.decode_s1(input_tokens[0], input_tokens[1], current_stamp)
  s1_logits = s1_logits[:, -1, :]
@@ -429,12 +441,28 @@ def get_dynamic_stamp(x_stamp, y_stamp, current_seq_len, pred_step):
  s2_logits = s2_logits[:, -1, :]
  sample_post = sample_from_logits(s2_logits, temperature=T, top_k=top_k, top_p=top_p, sample_logits=True)
 
- x_token[0] = torch.cat([x_token[0], sample_pre], dim=1)
- x_token[1] = torch.cat([x_token[1], sample_post], dim=1)
+ generated_pre[:, i] = sample_pre.squeeze(-1)
+ generated_post[:, i] = sample_post.squeeze(-1)
 
- input_tokens = [t[:, -max_context:].contiguous() for t in x_token]
+ if current_seq_len < max_context:
+ pre_buffer[:, current_seq_len] = sample_pre.squeeze(-1)
+ post_buffer[:, current_seq_len] = sample_post.squeeze(-1)
+ else:
+ pre_buffer.copy_(torch.roll(pre_buffer, shifts=-1, dims=1))
+ post_buffer.copy_(torch.roll(post_buffer, shifts=-1, dims=1))
+ pre_buffer[:, -1] = sample_pre.squeeze(-1)
+ post_buffer[:, -1] = sample_post.squeeze(-1)
+
+ full_pre = torch.cat([x_token[0], generated_pre], dim=1)
+ full_post = torch.cat([x_token[1], generated_post], dim=1)
+
+ context_start = max(0, total_seq_len - max_context)
+ input_tokens = [
+ full_pre[:, context_start:total_seq_len].contiguous(),
+ full_post[:, context_start:total_seq_len].contiguous()
+ ]
  z = tokenizer.decode(input_tokens, half=True)
- z = z.reshape(batch_size, sample_count, z.size(1), z.size(2))
+ z = z.reshape(-1, sample_count, z.size(1), z.size(2))
  preds = z.cpu().numpy()
  preds = np.mean(preds, axis=1)