Fix sdpa with batch size = 1, better benchmark

optimum/bettertransformer/models/decoder_models.py

@@ -58,6 +58,12 @@ def wrapped_scaled_dot_product(
  mask_value = torch.finfo(value.dtype).min
  mask_value = torch.full([], mask_value, dtype=value.dtype)
 
+ # in gpt-neo-x and gpt-j the query and keys are always in fp32
+ # thus we need to cast them to the value dtype
+ if self.downcast_qk:
+ query = query.to(value.dtype)
+ key = key.to(value.dtype)
+
  if batch_size == 1 and attention_mask is not None and attention_mask[0, 0, 0, -1] < -1:
  raise ValueError("BetterTransformer does not support padding='max_length' with a batch size of 1.")
 
@@ -86,16 +92,15 @@ def wrapped_scaled_dot_product(
  causal_mask = causal_mask.expand(batch_size, -1, -1, -1)
  attention_mask = causal_mask + attention_mask
 
- # in gpt-neo-x and gpt-j the query and keys are always in fp32
- # thus we need to cast them to the value dtype
- if self.downcast_qk:
- query = query.to(value.dtype)
- key = key.to(value.dtype)
-
  sdpa_result = torch.nn.functional.scaled_dot_product_attention(
  query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
  )
 
+ # in gpt-neo-x and gpt-j the query and keys are always in fp32
+ # thus we need to cast them to the value dtype
+ if self.downcast_qk:
+ sdpa_result = sdpa_result.to(value.dtype)
+
  return sdpa_result, None
 
  def forward(self, *args, **kwargs):
@@ -201,6 +206,11 @@ def wrapped_scaled_dot_product(
  if batch_size == 1 and attention_mask is not None and attention_mask[0, 0, 0, -1] < -1:
  raise ValueError("BetterTransformer does not support padding='max_length' with a batch size of 1.")
 
+ # in codegen the query and key are always in fp32 regardless of the dtype of the model
+ # https://github.com/huggingface/transformers/blob/5b28b7833297adf65c5160a685425ddb1eee5ce2/src/transformers/models/codegen/modeling_codegen.py#L226
+ query = query.to(value.dtype)
+ key = key.to(value.dtype)
+
  if batch_size == 1:
  if query.shape[2] > 1:
  # first step of the decoding
@@ -232,11 +242,6 @@ def wrapped_scaled_dot_product(
  # we use torch.min to avoid having tensor(-inf)
  attention_mask = torch.min(causal_mask, attention_mask)
 
- # in codegen the query and key are always in fp32 regardless of the dtype of the model
- # https://github.com/huggingface/transformers/blob/5b28b7833297adf65c5160a685425ddb1eee5ce2/src/transformers/models/codegen/modeling_codegen.py#L226
- query = query.to(value.dtype)
- key = key.to(value.dtype)
-
  sdpa_result = torch.nn.functional.scaled_dot_product_attention(
  query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
  )
@@ -381,6 +386,8 @@ def forward(
  super().forward_checker()
  raise_on_head_mask(layer_head_mask)
 
+ if output_attentions is True:
+ raise ValueError("output_attentions=True can not be supported with BetterTransformer.")
  if len(self.orig_layer.pruned_heads) > 0:
  raise ValueError(
  f"Setting `pruned_heads` is unsupported with BetterTransformer, found {self.orig_layer.pruned_heads}."
@@ -451,58 +458,52 @@ def project(hidden_states, proj_layer, key_value_states, past_key_value):
  past_key_value[1] if past_key_value is not None else None,
  )
 
- if (position_bias is None and not self.orig_layer.has_relative_attention_bias) or (
- position_bias is not None and position_bias[0, 0, 0, 0] == 0
- ):
- if position_bias is None and not self.orig_layer.has_relative_attention_bias:
+ query_states = self.scale * query_states
+ if position_bias is None and not self.orig_layer.has_relative_attention_bias:
+ if mask is None:
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states, key_states, value_states, attn_mask=None, dropout_p=0.0, is_causal=False
+ )
+ elif mask is not None:
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states, key_states, value_states, attn_mask=mask, dropout_p=0.0, is_causal=False
+ )
+
+ if position_bias is None:
+ if not self.orig_layer.has_relative_attention_bias:
  position_bias = torch.zeros(
  (1, self.orig_layer.n_heads, real_seq_length, key_length),
- device=query_states.device,
- dtype=query_states.dtype,
+ device=value_states.device,
+ dtype=value_states.dtype,
  )
  if self.orig_layer.gradient_checkpointing and self.orig_layer.training:
  position_bias.requires_grad = True
+ else:
+ position_bias = self.orig_layer.compute_bias(real_seq_length, key_length, device=value_states.device)
 
- query_states = self.scale * query_states
- attn_output = torch.nn.functional.scaled_dot_product_attention(
- query_states, key_states, value_states, attn_mask=None, dropout_p=0.0, is_causal=False
- )
-
- else:
- # compute scores
- scores = torch.matmul(
- query_states, key_states.transpose(3, 2)
- ) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
-
- if position_bias is None:
- position_bias = self.orig_layer.compute_bias(real_seq_length, key_length, device=scores.device)
-
- # if key and values are already calculated
- # we want only the last query position bias
- if past_key_value is not None:
- position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
-
- if mask is not None:
- position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length)
+ # if key and values are already calculated
+ # we want only the last query position bias
+ if past_key_value is not None:
+ position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
 
- scores += position_bias
- attn_weights = torch.nn.functional.softmax(scores.float(), dim=-1).type_as(
- scores
- ) # (batch_size, n_heads, seq_length, key_length)
- attn_weights = torch.nn.functional.dropout(
- attn_weights, p=self.orig_layer.dropout, training=self.orig_layer.training
- ) # (batch_size, n_heads, seq_length, key_length)
+ if mask is not None:
+ position_bias = position_bias + mask # (batch_size, n_heads, seq_length, key_length)
 
- attn_output = torch.matmul(attn_weights, value_states)
+ if self.orig_layer.has_relative_attention_bias:
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states, key_states, value_states, attn_mask=position_bias, dropout_p=0.0, is_causal=False
+ )
+ else:
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states, key_states, value_states, attn_mask=position_bias, dropout_p=0.0, is_causal=False
+ )
 
  attn_output = unshape(attn_output) # (batch_size, seq_length, dim)
  attn_output = self.orig_layer.o(attn_output)
 
  present_key_value_state = (key_states, value_states) if (self.orig_layer.is_decoder and use_cache) else None
  outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
 
- if output_attentions:
- outputs = outputs + (attn_weights,)
  return outputs
 
 

tests/benchmark/benchmark_bettertransformer.py

@@ -2,9 +2,10 @@
 
 import torch
 from tqdm import tqdm
-from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers import AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
 
 from optimum.bettertransformer import BetterTransformer
+from optimum.exporters import TasksManager
 
 
 def get_parser():
@@ -104,57 +105,55 @@ def get_batch(batch_size, avg_seqlen, max_sequence_length, seqlen_stdev, vocab_s
 def timing_cuda(model, num_batches, input_ids, masks, is_decoder, generation_config=None):
  start_event = torch.cuda.Event(enable_timing=True)
  end_event = torch.cuda.Event(enable_timing=True)
+
+ torch.cuda.reset_peak_memory_stats(device)
+ torch.cuda.empty_cache()
+ torch.cuda.synchronize()
+
  start_event.record()
- for _ in range(num_batches):
+ for _ in tqdm(range(num_batches)):
  if is_decoder:
  _ = model.generate(input_ids, attention_mask=masks, generation_config=generation_config)
  else:
  _ = model(input_ids, masks)
  end_event.record()
  torch.cuda.synchronize()
- return (start_event.elapsed_time(end_event) * 1.0e-3) / num_batches
+ max_memory = torch.cuda.max_memory_allocated(device)
 
+ return (start_event.elapsed_time(end_event) * 1.0e-3) / num_batches, max_memory
 
-def benchmark(hf_model, bt_model, input_ids, masks, num_batches, is_decoder, max_token, pad_token_id):
+
+def benchmark(model, input_ids, masks, num_batches, is_decoder, max_token, pad_token_id):
  # Warmup
  if is_decoder:
- min_length = max(max_token - 20, 5)
-
  gen_config = GenerationConfig(
- do_greedy=True,
  max_new_tokens=max_token,
- min_length=min_length,
+ min_new_tokens=max_token,
  use_cache=True,
  pad_token_id=pad_token_id,
  )
- _ = hf_model.generate(input_ids, attention_mask=masks, generation_config=gen_config)
- torch.cuda.synchronize()
- bt_model.generate(input_ids, attention_mask=masks, generation_config=gen_config)
+ _ = model.generate(input_ids, attention_mask=masks, generation_config=gen_config)
  torch.cuda.synchronize()
 
  else:
- _ = hf_model(input_ids, masks)
- torch.cuda.synchronize()
- _ = bt_model(input_ids, masks)
+ _ = model(input_ids, masks)
  torch.cuda.synchronize()
 
  # benchmark
  if is_decoder:
- total_hf_time = timing_cuda(hf_model, num_batches, input_ids, masks, is_decoder, gen_config)
- total_bt_time = timing_cuda(bt_model, num_batches, input_ids, masks, is_decoder, gen_config)
+ total_time, max_mem = timing_cuda(model, num_batches, input_ids, masks, is_decoder, gen_config)
  else:
- total_hf_time = timing_cuda(hf_model, num_batches, input_ids, masks, is_decoder)
- total_bt_time = timing_cuda(bt_model, num_batches, input_ids, masks, is_decoder)
+ total_time, max_mem = timing_cuda(model, num_batches, input_ids, masks, is_decoder)
 
- return total_bt_time, total_hf_time
+ return total_time, max_mem
 
 
 if __name__ == "__main__":
  parser = get_parser()
  args = parser.parse_args()
 
- BATCH_SIZES = [8, 16, 64]
- SEQ_LEN = [64, 128, 256]
+ BATCH_SIZES = [2]
+ SEQ_LEN = [64]
  if args.is_decoder:
  PAD_PERCENTAGES = [0]
  else:
@@ -166,63 +165,98 @@ def benchmark(hf_model, bt_model, input_ids, masks, num_batches, is_decoder, max
  if not hasattr(tokenizer, "pad_token") or tokenizer.pad_token is None:
  tokenizer.pad_token = tokenizer.eos_token
 
- if args.is_decoder:
- hf_model = AutoModelForCausalLM.from_pretrained(
- args.model_name, torch_dtype=torch.float16 if args.use_half else None, use_cache=True
- ).eval()
+ task = TasksManager.infer_task_from_model(args.model_name)
+
+ if task == "causal-lm":
+ autoclass = AutoModelForCausalLM
+ elif task == "seq2seq-lm":
+ autoclass = AutoModelForSeq2SeqLM
  else:
- hf_model = AutoModel.from_pretrained(
- args.model_name, torch_dtype=torch.float16 if args.use_half else None
- ).eval()
+ autoclass = AutoModel
 
  if args.use_cuda:
- hf_model = hf_model.to(0)
+ with torch.device("cuda:0"):
+ hf_model = autoclass.from_pretrained(args.model_name, torch_dtype=torch.float16 if args.use_half else None)
+ # in PyTorch we trust :)
+ hf_model = hf_model.to("cuda:0")
+ hf_model = hf_model.to(torch.float16)
+ else:
+ hf_model = autoclass.from_pretrained(args.model_name, torch_dtype=torch.float16 if args.use_half else None)
+
  bt_model = BetterTransformer.transform(hf_model, keep_original_model=True)
 
  output_file = open("log_{}.csv".format(args.model_name.replace("/", "-")), "w")
  output_file.write(
- "num_batches, batch_size, seq_len, is cuda, is half, use mask, pad percentage, HF time, BT time, Speedup\n"
+ "num_batches, batch_size, seq_len, is cuda, is half, use mask, pad percentage, HF time, BT time, Speedup, Mem eager (MB), Mem BT (MB), Mem saved\n"
  )
  for bs in tqdm(BATCH_SIZES):
  for seq_len in tqdm(SEQ_LEN):
  for pad_perc in tqdm(PAD_PERCENTAGES):
+ print(f"-- Running: bs={bs}, seq_len={seq_len}")
  # current_std = int(seq_len*pad_perc)
  # max_seqlen = seq_len + current_std
  max_seqlen = seq_len
  mean_seqlen = int((1 - pad_perc) * max_seqlen)
- input_ids, _, masks = get_batch(bs, mean_seqlen, max_seqlen, args.seqlen_stdev)
+ input_ids, _, masks = get_batch(
+ bs, mean_seqlen, max_seqlen, args.seqlen_stdev, vocab_size=hf_model.config.vocab_size
+ )
 
  if args.use_cuda:
  input_ids = input_ids.to(device)
  masks = masks.to(device)
- if not args.use_mask:
+
+ if args.use_mask is False and bs == 1:
  masks = None
 
- total_bt_time, total_hf_time = benchmark(
- hf_model,
- bt_model,
- input_ids,
- masks,
- args.num_batches,
- args.is_decoder,
- args.max_token,
- tokenizer.pad_token_id,
- )
+ with torch.inference_mode():
+ total_hf_time, max_mem_eager = benchmark(
+ hf_model,
+ input_ids,
+ masks,
+ args.num_batches,
+ args.is_decoder,
+ args.max_token,
+ tokenizer.pad_token_id,
+ )
+
+ # raise error if no optimized kernel is available
+ with torch.backends.cuda.sdp_kernel(
+ enable_flash=True, enable_math=True, enable_mem_efficient=True
+ ):
+ total_bt_time, max_mem_bt = benchmark(
+ bt_model,
+ input_ids,
+ masks,
+ args.num_batches,
+ args.is_decoder,
+ args.max_token,
+ tokenizer.pad_token_id,
+ )
 
  speedup = total_hf_time / total_bt_time
+ mem_saved = max_mem_eager / max_mem_bt
+
+ max_mem_eager = max_mem_eager * 1e-6
+ max_mem_bt = max_mem_bt * 1e-6
+
+ print(f"PT eager: {total_hf_time:.3f} s, peak {max_mem_eager:.2f} MB")
+ print(f"PT native: {total_bt_time:.3f} s, peak {max_mem_bt:.2f} MB")
 
  output_file.write(
- "{},{},{},{},{},{},{},{},{},{}\n".format(
+ "{},{},{},{},{},{},{},{},{},{},{},{},{}\n".format(
  args.num_batches,
  args.use_cuda,
  bs,
  seq_len,
  args.use_half,
  args.use_mask,
  pad_perc,
- total_hf_time,
- total_bt_time,
- speedup,
+ f"{total_hf_time:.3f}",
+ f"{total_bt_time:.3f}",
+ f"{speedup:.3f}",
+ f"{max_mem_eager:.3f}",
+ f"{max_mem_bt:.3f}",
+ f"{mem_saved:.3f}",
  )
  )
  output_file.close()