import torch import time from dataclasses import dataclass from opentelemetry import trace from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase from typing import Optional, Tuple, List, Type, Dict from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.models import Model from text_generation_server.utils.chunks import concat_text_chunks from text_generation_server.utils.tokens import batch_top_tokens from text_generation_server.models.types import ( Batch, Tokens, Generation, GeneratedText, ) from text_generation_server.pb import generate_pb2 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch from text_generation_server.utils.import_utils import ( empty_cache, synchronize, get_free_memory, ) from text_generation_server.utils.speculate import get_speculate from text_generation_server.utils.dist import MEMORY_FRACTION tracer = trace.get_tracer(__name__) from transformers.cache_utils import PagedCache from loguru import logger # Why define it here? BLOCK_SIZE: int = 16 class CausalLMRagged(Model): def __init__( self, model_id: str, revision: Optional[str] = None, quantize: Optional[str] = None, speculator: Optional[str] = None, dtype: Optional[torch.dtype] = None, trust_remote_code: bool = False, ): if speculator: raise RuntimeError("Speculator decoding is not enabled for AutoModel") if torch.cuda.is_available(): device = torch.device("cuda:0") # TODO felix: fix support for accelerate dtype = torch.float16 if dtype is None else dtype else: if quantize: raise ValueError("quantization is not available on CPU") device = torch.device("cpu") dtype = torch.float32 if dtype is None else dtype tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) model = AutoModelForCausalLM.from_pretrained( model_id, revision=revision, torch_dtype=dtype, device_map=None, load_in_8bit=quantize == "bitsandbytes", trust_remote_code=trust_remote_code, attn_implementation="flash_attention_2", ) if ( torch.cuda.is_available() and torch.cuda.device_count() == 1 and quantize != "bitsandbytes" ): model = model.cuda() self.kv_cache = [] self.num_layers = len(model.model.layers) self.num_kv_heads = model.config.num_key_value_heads self.head_size = model.config.hidden_size // model.config.num_attention_heads if tokenizer.pad_token_id is None: if model.config.pad_token_id is not None: tokenizer.pad_token_id = model.config.pad_token_id elif model.config.eos_token_id is not None: tokenizer.pad_token_id = model.config.eos_token_id elif tokenizer.eos_token_id is not None: tokenizer.pad_token_id = tokenizer.eos_token_id else: tokenizer.add_special_tokens({"pad_token": "[PAD]"}) super().__init__( model_id=model_id, model=model, tokenizer=tokenizer, requires_padding=False, dtype=dtype, device=device, ) def warmup(self, batch: FlashCausalLMBatch): # The warmup batch is the biggest batch we could ever receive empty_cache() try: self.init_kv_cache( batch.num_blocks, self.num_layers, self.num_kv_heads, self.head_size, self.dtype, self.device, ) max_bt = batch.max_blocks max_s = max_bt * BLOCK_SIZE _, batch, _ = self.generate_token(batch) except torch.cuda.OutOfMemoryError as e: raise RuntimeError( f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. " f"You need to decrease `--max-batch-prefill-tokens`" ) from e synchronize(self.device) # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm) # Calculate the number of blocks that can be allocated with the free memory dtype_size = torch.tensor([], dtype=self.dtype).element_size() cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size free_memory = get_free_memory(self.device, MEMORY_FRACTION) batch_num_blocks = batch.num_blocks if batch is not None else 0 num_blocks = ( # Leave 5% for some wiggle room int((free_memory * 0.95) // total_cache_size) # Add batch.num_blocks as we allocated it above, so it is included in the peak memory. + batch_num_blocks ) del batch self.init_kv_cache( num_blocks, self.num_layers, self.num_kv_heads, self.head_size, self.dtype, self.device, ) return int(num_blocks * BLOCK_SIZE) def init_kv_cache( self, num_blocks: int, num_layers: int, num_heads: int, head_size: int, dtype: torch.dtype, device: torch.device, ): self.kv_cache = [] empty_cache() element_size = torch.tensor([], dtype=dtype).element_size() if SYSTEM == "ipex" and device.type == "xpu": raise ValueError("Untested. Please open an issue") else: x = BLOCK_SIZE // element_size if SYSTEM == "ipex" and device == torch.device("cpu"): raise ValueError("Untested. Please open an issue") self.kv_cache = [ ( torch.empty( (num_blocks, num_heads, head_size // x, BLOCK_SIZE, x), dtype=dtype, device=device, ), torch.empty( (num_blocks, num_heads, head_size, BLOCK_SIZE), dtype=dtype, device=device, ), ) for _ in range(num_layers) ] @property def batch_type(self) -> Type[FlashCausalLMBatch]: return FlashCausalLMBatch def decode(self, generated_ids: List[int]) -> str: return self.tokenizer.decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) def forward( self, batch: FlashCausalLMBatch ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: # NOTE: adapter_data: not supported input_ids = batch.input_ids position_ids = batch.position_ids cu_seqlen_prefill = batch.cu_seqlen_prefill kv_cache = self.kv_cache block_tables = batch.block_tables_tensor slots = batch.slots[batch.slot_indices] input_lengths = batch.input_lengths_tensor max_s = batch.max_seqlen lm_head_indices = batch.prefill_head_indices # TODO felix: support window attention # if cu_seqlen_prefill is None and self.max_past() is not None: # # In decode, not prefill, we're actually overwriting the KV-cache # # in a circular buffer mode. # # This makes sure the max_s for the decode pass is correct. # max_s = min(self.max_past(), max_s) bs = input_ids.shape[0] logits = self.model.forward( input_ids=input_ids, position_ids=position_ids, past_key_values=PagedCache(), cu_seqlen_prefill=cu_seqlen_prefill, kv_cache=kv_cache, block_tables=block_tables, slots=slots, input_lengths=input_lengths, max_s=max_s, prefill_cache_indices=batch.prefill_cache_indices, lm_head_indices=lm_head_indices, cache_position=False, return_dict=False, )[0] if lm_head_indices is not None: logits = logits[lm_head_indices] if batch.prefill_cache_indices is not None: batch.prefill_cache_indices = None speculative_logits = None return logits, speculative_logits @tracer.start_as_current_span("generate_token") def generate_token( self, batch: FlashCausalLMBatch ) -> Tuple[List[Generation], Optional[FlashCausalLMBatch], Tuple[int, int]]: start = time.time_ns() prefill = batch.cu_seqlen_prefill is not None prefill_logprobs = batch.prefill_next_token_indices is not None # Update adapter indices for speculative tokens (if present) # adapter_meta = batch.adapter_meta # if batch.speculative_ids is not None: # B, speculative_length = batch.speculative_ids.shape # new_length = speculative_length + 1 # adapter_indices = ( # adapter_meta.adapter_indices.unsqueeze(-1) # .expand(B, new_length) # .reshape(-1) # ) # adapter_segments = adapter_meta.adapter_segments * new_length # adapter_meta = AdapterBatchMetadata( # adapter_indices=adapter_indices, # adapter_set=adapter_meta.adapter_set, # adapter_segments=adapter_segments, # segment_indices=adapter_meta.segment_indices, # ) # Assign pointers to adapter weights # TODO(travis): don't update this if indices haven't changed # adapter_data = AdapterBatchData.from_meta( # adapter_meta, # self.layer_to_adapter_weights, # prefill, # batch.prefill_head_indices, # ) logger.info(f"batch.input_ids {batch.input_ids}") out, speculative_logits = self.forward(batch) logger.info(f"out {out.shape}") logger.info(f"speculative_logits {speculative_logits}") if prefill: next_token_logits = ( out[batch.prefill_next_token_indices] if prefill_logprobs else out ) if speculative_logits is not None: speculative_logits = ( speculative_logits[batch.prefill_next_token_indices] if prefill_logprobs else speculative_logits ) # next_adapter_indices = batch.adapter_meta.adapter_indices.new_empty( # len(batch) # ) else: next_token_logits = out # next_adapter_indices = batch.adapter_meta.adapter_indices speculate = get_speculate() ( next_input_ids, next_token_logprobs, logprobs, accepted_ids, speculative_ids, ) = batch.next_token_chooser( batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits, speculate, batch.speculative_ids, speculative_logits, ) batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens( batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs, accepted_ids ) if prefill: if len(batch) > 1 and prefill_logprobs: # We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs # When batch == 1, we will just use the batch.input_ids values directly prefill_tokens_indices = batch.input_ids.new_zeros(len(out)) next_position_ids = batch.position_ids.new_empty(len(batch)) batch.slot_indices = batch.slot_indices[batch.cu_seqlen_prefill[1:] - 1] # We do not need cu_seqlen_prefill anymore batch.cu_seqlen_prefill = None else: prefill_logprobs = None next_position_ids = batch.position_ids # Cumulative length cumulative_length = 0 # Results generations: List[Generation] = [] stopped = True # Zipped iterator iterator = zip(batch.input_lengths, batch.all_input_ids, accepted_ids) # We do two for loops as the first one can run completely asynchronously from the GPU while for the second # one, we need to first do a GPU <-> CPU sync # It is faster if we delay this sync for the maximum amount of time # For each member of the batch index = 0 for i, (input_length, all_input_ids, n_accepted_ids) in enumerate(iterator): # Indexing metadata start_index = cumulative_length end_index = cumulative_length + input_length if prefill: # Indexing metadata out_start_index = batch.prefill_cu_outlens[i] out_end_index = batch.prefill_cu_outlens[i + 1] out_length = out_end_index - out_start_index # Initialize position_ids # In decode, we do not need this as we can just increment position ids next_position_ids[i] = batch.position_ids[end_index - 1] # Initialize adapter indices # In decode, we only have one token per row in the batch, so grab last index # next_adapter_indices[i] = batch.adapter_meta.adapter_indices[ # end_index - 1 # ] # Used to gather prefill logprobs # Copy batch.input_ids to prefill_token_indices if prefill_logprobs: if len(batch) > 1: prefill_tokens_indices[out_start_index : out_end_index - 1] = ( batch.input_ids[start_index + 1 : start_index + out_length] ) else: # Set prefill_tokens_indices to the correct slice prefill_tokens_indices = batch.input_ids[ start_index + 1 : start_index + out_length ] for j in range(n_accepted_ids): batch.all_input_ids_tensor[i, input_length + j] = next_input_ids[index] index += 1 cumulative_length += input_length logger.info(f"batch.input_lengths_tensor {batch.input_lengths_tensor}") logger.info(f"accepted_ids {accepted_ids}") logger.info(f"batch.all_input_ids {batch.all_input_ids}") # Update values batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1] batch.speculative_ids = speculative_ids batch.position_ids = next_position_ids + accepted_ids batch.input_lengths_tensor += accepted_ids batch.slot_indices += accepted_ids # batch.adapter_meta.adapter_indices = None # if prefill: # # adjust segment lengths to account for all request lengths being 1 during decoding # adapter_segments, _ = find_segments(batch.adapter_meta.adapter_indices) # batch.adapter_meta.adapter_segments = torch.tensor( # adapter_segments, # dtype=torch.int32, # device=batch.adapter_meta.adapter_segments.device, # ) if prefill and prefill_logprobs: # Get prefill logprobs prefill_logprobs_tensor = torch.log_softmax(out, -1) prefill_logprobs = torch.gather( prefill_logprobs_tensor, 1, prefill_tokens_indices.view(-1, 1) ) # GPU <-> CPU sync prefill_logprobs = prefill_logprobs.view(-1).tolist() # GPU <-> CPU sync next_token_logprobs = next_token_logprobs.tolist() next_token_ids = next_input_ids.tolist() accepted_ids = accepted_ids.tolist() start_decode = time.time_ns() # Zipped iterator iterator = zip( batch.requests, batch.input_lengths, batch.prefix_offsets, batch.read_offsets, batch.stopping_criterias, batch.all_input_ids, batch.next_token_chooser.do_sample, batch.next_token_chooser.seeds, batch.top_n_tokens, accepted_ids, batch_top_token_ids, batch_top_token_logprobs, ) # For each member of the batch index = 0 for i, ( request, input_length, prefix_offset, read_offset, stopping_criteria, all_input_ids, do_sample, seed, top_n_tokens, n_accepted_ids, top_token_ids, top_token_logprobs, ) in enumerate(iterator): # Append next token to all tokens next_token_texts = [] left = 0 if n_accepted_ids > 1: if RANK == 0: logger.debug(f"Speculated ids {n_accepted_ids - 1}") current_stopped = False for j in range(index, index + n_accepted_ids): # Generated token next_token_id = next_token_ids[j] all_input_ids.append(next_token_id) next_token_text, prefix_offset, read_offset = self.decode_token( all_input_ids, prefix_offset, read_offset, ) next_token_texts.append(next_token_text) stop, reason = stopping_criteria( next_token_id, next_token_text, ) if stop: left = index + n_accepted_ids - j - 1 current_stopped = True break else: current_stopped = False stopped = stopped and current_stopped _next_token_ids = next_token_ids[index : index + n_accepted_ids - left] _next_token_logprobs = next_token_logprobs[ index : index + n_accepted_ids - left ] index += n_accepted_ids # Shard generations # All generations will be appended in the rust sharded client if i % self.world_size == self.rank: if stop: # Decode generated tokens output_text, _, _ = self.decode_token( all_input_ids, prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1, read_offset=len(all_input_ids) - stopping_criteria.current_tokens, skip_special_tokens=True, ) generated_text = GeneratedText( output_text, stopping_criteria.current_tokens, reason, seed if do_sample else None, ) else: generated_text = None # Prefill if prefill and request.prefill_logprobs: out_start_index = batch.prefill_cu_outlens[i] out_end_index = batch.prefill_cu_outlens[i + 1] # Remove generated token to only have prefill and add nan for first prompt token request_prefill_logprobs = [float("nan")] + prefill_logprobs[ out_start_index : out_end_index - 1 ] prefill_token_ids = all_input_ids[:-1] prefill_texts = self.tokenizer.batch_decode( prefill_token_ids, clean_up_tokenization_spaces=False, skip_special_tokens=False, ) prefill_tokens = Tokens( prefill_token_ids, request_prefill_logprobs, prefill_texts, is_special=[], ) else: prefill_tokens = None if top_n_tokens > 0: all_top_tokens = [] for top_token_ids, top_token_logprobs in zip( top_token_ids, top_token_logprobs ): toptoken_texts = self.tokenizer.batch_decode( top_token_ids, clean_up_tokenization_spaces=False, skip_special_tokens=False, ) special_toptokens = [ token_id in self.all_special_ids for token_id in top_token_ids ] top_tokens = Tokens( top_token_ids, top_token_logprobs, toptoken_texts, special_toptokens, ) all_top_tokens.append(top_tokens) top_tokens = all_top_tokens else: top_tokens = None generation = Generation( request.id, prefill_tokens, Tokens( _next_token_ids, _next_token_logprobs, next_token_texts, [nid in self.all_special_ids for nid in _next_token_ids], ), generated_text, top_tokens, ) generations.append(generation) # accept each new token for this specific request since we may # have more than one new token per request with speculative decoding for next_token_id in _next_token_ids: batch.next_token_chooser = ( batch.next_token_chooser.advance_grammar_single(i, next_token_id) ) # Update values batch.input_lengths[i] = input_length + n_accepted_ids if batch.input_lengths[i] > batch.max_seqlen: batch.max_seqlen = batch.input_lengths[i] batch.prefix_offsets[i] = prefix_offset batch.read_offsets[i] = read_offset batch.all_input_ids[i] = all_input_ids if stopped: # No need to return a batch if we know that all requests stopped forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, None, (forward_ns, decode_ns) batch.prefill_cu_outlens = None batch.prefill_head_indices = None batch.prefill_next_token_indices = None forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, batch, (forward_ns, decode_ns)