From 60f63262dbcef05da186278278ccacc8dc6e2421 Mon Sep 17 00:00:00 2001 From: Karol Damaszke Date: Fri, 19 Jan 2024 15:18:35 +0100 Subject: [PATCH] Prefill optimization by allocating space only for the first token (#17) --- .../models/causal_lm.py | 59 +++++++++++++------ server/text_generation_server/server.py | 4 +- 2 files changed, 44 insertions(+), 19 deletions(-) diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py index bc4a0366..6755184f 100644 --- a/server/text_generation_server/models/causal_lm.py +++ b/server/text_generation_server/models/causal_lm.py @@ -114,6 +114,15 @@ def shift_all(srcs, dim, offsets): return [shift(src, dim, offset) for src, offset in zip(srcs, offsets)] +def pad_tensors(tensors, paddings, dim, value): + for i, (tensor, padding) in enumerate(zip(tensors, paddings)): + if padding > 0: + pad_shape = (0, 0, 0, padding) if dim == -2 else (0, padding) + tensors[i] = torch.nn.functional.pad(tensor, pad_shape, value=value) + htorch.core.mark_step() + return tensors + + @dataclass class CausalLMRequest: idx: int @@ -170,7 +179,7 @@ class CausalLMBatch(Batch): ) @classmethod - def recombine(cls, batches: List["CausalLMBatch"], is_optimized_for_gaudi: bool = False) -> "CausalLMBatch": + def recombine(cls, batches: List["CausalLMBatch"], pad_token_id: int) -> "CausalLMBatch": total_requests = sum(len(b) for b in batches) new_bs = round_up(total_requests, BATCH_BUCKET_SIZE) batch_id = batches[0].batch_id @@ -212,10 +221,6 @@ class CausalLMBatch(Batch): to_tensors = lambda ind: (torch.tensor(ind[0], device=device), torch.tensor(ind[1], device=device)) indices = [[to_tensors(req.update_idx(next(free_indices))) for req in batch_reqs] for batch_reqs in grouped_requests] - max_seq_len = batches[0].attention_mask.size(1) - input_length = max_input_length - right_padding = max_seq_len - input_length - chunk_size = batches[0].past_key_values[0][0].size(0) // batches[0].batch_size num_layers = len(batches[0].past_key_values) past_key_values_type = type(batches[0].past_key_values) @@ -231,9 +236,14 @@ class CausalLMBatch(Batch): for b in batches: b.past_key_values = list(b.past_key_values) + # For prefill there is a space allocated only for first token + # Need to add padding to the max total tokens before first decode + paddings = [(batch.input_length + batch.right_padding) - batch.seq_length for batch in batches] + src = [b.input_ids for b in batches] for b in batches: del b.input_ids + src = pad_tensors(src, paddings, seq_dim, pad_token_id) src = shift_all(src, seq_dim, offsets) input_ids = prepare_memory(new_bs, src[target_batch_idx], inplace) input_ids = move_data(input_ids, 1, indices, src) @@ -241,6 +251,7 @@ class CausalLMBatch(Batch): src = [b.attention_mask for b in batches] for b in batches: del b.attention_mask + src = pad_tensors(src, paddings, seq_dim, 0) src = shift_all(src, seq_dim, offsets) attention_mask = prepare_memory(new_bs, src[target_batch_idx], inplace) attention_mask = move_data(attention_mask, 1, indices, src) @@ -255,11 +266,13 @@ class CausalLMBatch(Batch): past_key_values = [] for layer_num in range(num_layers): src = [b.past_key_values[layer_num][0] for b in batches] + src = pad_tensors(src, paddings, key_dim, 0) src = shift_all(src, key_dim, offsets) updated_key = prepare_memory(new_bs * chunk_size, src[target_batch_idx], inplace) updated_key = move_data(updated_key, chunk_size, indices, src) src = [b.past_key_values[layer_num][1] for b in batches] + src = pad_tensors(src, paddings, value_dim, 0) src = shift_all(src, value_dim, offsets) updated_value = prepare_memory(new_bs * chunk_size, src[target_batch_idx], inplace) updated_value = move_data(updated_value, chunk_size, indices, src) @@ -278,6 +291,10 @@ class CausalLMBatch(Batch): batches[0].next_token_chooser.dtype ) + max_seq_len = attention_mask.size(1) + input_length = max_input_length + right_padding = max_seq_len - input_length + htorch.core.mark_step() return cls( @@ -352,12 +369,16 @@ class CausalLMBatch(Batch): attention_mask = tokenized_inputs["attention_mask"] if is_optimized_for_gaudi: + # Allocate space for first token input_ids = torch.nn.functional.pad( - input_ids, (0, max_new_tokens + extra_padding), value=tokenizer.pad_token_id + input_ids, (0, 1), value=tokenizer.pad_token_id ) attention_mask = torch.nn.functional.pad( - attention_mask, (0, max_new_tokens + extra_padding), value=0) - all_input_ids = input_ids.T.split(1, dim=1) + attention_mask, (0, 1), value=0 + ) + all_input_ids = torch.nn.functional.pad( + input_ids, (0, max_new_tokens + extra_padding - 1), value=tokenizer.pad_token_id + ).T.split(1, dim=1) else: all_input_ids = input_ids.clone().T.split(1, dim=1) @@ -386,16 +407,16 @@ class CausalLMBatch(Batch): ) @tracer.start_as_current_span("filter") - def filter(self, request_ids: List[int], is_optimized_for_gaudi: bool = False) -> Optional["CausalLMBatch"]: + def filter(self, request_ids: List[int], pad_token_id: int = 0) -> Optional["CausalLMBatch"]: dbg_trace('FILTER', f'num_reqs:{len(self.requests)} -> {len(request_ids)}') request_ids = set(request_ids) self.requests = [req for req in self.requests if req.data.id in request_ids] - return self.__class__.recombine([self], is_optimized_for_gaudi) + return self.__class__.recombine([self], pad_token_id) @classmethod @tracer.start_as_current_span("concatenate") - def concatenate(cls, batches: List["CausalLMBatch"], is_optimized_for_gaudi: bool = False) -> "CausalLMBatch": - return cls.recombine(batches, is_optimized_for_gaudi) + def concatenate(cls, batches: List["CausalLMBatch"], pad_token_id: int = 0) -> "CausalLMBatch": + return cls.recombine(batches, pad_token_id) def __len__(self): return len(self.requests) @@ -611,7 +632,7 @@ class CausalLM(Model): prefill = batch.past_key_values is None # Check if we need to do any bookkeeping first if not prefill: - batch = batch.__class__.recombine([batch], self.is_optimized_for_gaudi) + batch = batch.__class__.recombine([batch], self.tokenizer.pad_token_id) scenario = 'PREFILL' if prefill else 'GENERATE' dbg_trace(scenario, f'bs:{batch.batch_size} num_reqs:{len(batch.requests)} seq_len:{batch.seq_length}') @@ -621,16 +642,20 @@ class CausalLM(Model): self.hb_profer_started = False if self.is_optimized_for_gaudi: - token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device) + if prefill: + # no right padding for prefill + token_idx = torch.tensor(batch.attention_mask.shape[-1] - 1).to(self.device) + else: + token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device) attention_mask = batch.attention_mask else: token_idx = None # slice the attention mask to the correct shape # TODO fix me! attention_mask = batch.attention_mask[:, : -batch.padding_right_offset] - if batch.past_key_values: - if token_idx is not None: - input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1) + + if not prefill and token_idx is not None: + input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1) else: input_ids = batch.input_ids diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py index 1e17784e..e54f4610 100644 --- a/server/text_generation_server/server.py +++ b/server/text_generation_server/server.py @@ -59,7 +59,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): {"util": len(batch.requests)}): if batch is None: raise ValueError(f"Batch ID {request.batch_id} not found in cache.") - filtered_batch = batch.filter(request.request_ids, self.model.is_optimized_for_gaudi) + filtered_batch = batch.filter(request.request_ids, self.model.tokenizer.pad_token_id) self.cache.set(filtered_batch) return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb()) @@ -113,7 +113,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): if len(batches) > 1: with self.profiler.record_event("internal", "concatenate"): - batch = self.model.batch_type.concatenate(batches, self.model.is_optimized_for_gaudi) + batch = self.model.batch_type.concatenate(batches, self.model.tokenizer.pad_token_id) else: batch = batches[0]