import torch import torch.distributed from transformers import AutoTokenizer, PreTrainedTokenizerBase from typing import Optional from text_generation_server.models.custom_modeling.mamba_modeling import ( MambaConfig, ) from loguru import logger from text_generation_server.pb import generate_pb2 from text_generation_server.utils import ( initialize_torch_distributed, weight_files, Weights, ) from text_generation_server.models.globals import CUDA_GRAPHS, MEM_POOL import time from text_generation_server.models.custom_modeling.mamba_modeling import ( MambaModel, InferenceParams, ) from text_generation_server.models import Model from typing import Any, List, Tuple, Type, Dict from text_generation_server.models.types import ( Batch, Tokens, Generation, GeneratedText, ) from text_generation_server.utils.chunks import concat_text_chunks from text_generation_server.utils.quantization import get_loader from text_generation_server.utils.tokens import batch_top_tokens, Sampling from dataclasses import dataclass from text_generation_server.utils import NextTokenChooser, StoppingCriteria def new_inference_params( n_blocks: int, batch_size: int, d_inner: int, d_conv: int, d_state: int, seqlen_offset: int, dtype: torch.dtype, device: torch.device, ): max_seqlen = 0 conv_states = torch.zeros( ( n_blocks, batch_size, d_inner, d_conv, ), device=device, dtype=dtype, ) ssm_states = torch.zeros( ( n_blocks, batch_size, d_inner, d_state, ), device=device, dtype=dtype, ) inference_params = InferenceParams( max_seqlen=max_seqlen, max_batch_size=batch_size, seqlen_offset=seqlen_offset, conv_states=conv_states, ssm_states=ssm_states, ) return inference_params @dataclass class MambaBatch(Batch): batch_id: int requests: List[generate_pb2.Request] requests_idx_mapping: Dict[int, int] # Decoder values input_ids: torch.Tensor # All tokens all_input_ids: List[torch.Tensor] # Lengths of all generations present in the batch input_lengths: List[int] prefix_offsets: List[int] read_offsets: List[int] # Generation helpers next_token_choosers: List[NextTokenChooser] stopping_criterias: List[StoppingCriteria] top_n_tokens: List[int] top_n_tokens_tensor: torch.Tensor # Metadata used for padding max_input_length: int padding_right_offset: int # Maximum number of tokens this batch will grow to max_tokens: int # Past metadata keys_head_dim_last: bool = True # Inference params inference_params: Optional[Dict[str, Any]] = None def to_pb(self) -> generate_pb2.CachedBatch: return generate_pb2.CachedBatch( id=self.batch_id, request_ids=[r.id for r in self.requests], size=len(self), max_tokens=self.max_tokens, ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, ) -> "MambaBatch": inputs = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] prefix_offsets = [] read_offsets = [] requests_idx_mapping = {} # Parse batch max_truncation = 0 padding_right_offset = 0 max_decode_tokens = 0 for i, r in enumerate(pb.requests): requests_idx_mapping[r.id] = i inputs.append(concat_text_chunks(r.input_chunks.chunks)) next_token_choosers.append( NextTokenChooser.from_pb(r.parameters, device, tokenizer) ) stopping_criteria = StoppingCriteria.from_pb( r.stopping_parameters, tokenizer ) stopping_criterias.append(stopping_criteria) top_n_tokens.append(r.top_n_tokens) max_truncation = max(max_truncation, r.truncate) max_decode_tokens += stopping_criteria.max_new_tokens padding_right_offset = max( padding_right_offset, stopping_criteria.max_new_tokens ) tokenized_inputs = tokenizer( inputs, return_tensors="pt", padding=True, return_token_type_ids=False, truncation=True, max_length=max_truncation, ).to(device) for _ in pb.requests: input_len = tokenized_inputs["input_ids"].shape[1] prefix_offsets.append(input_len - 5) read_offsets.append(input_len) input_lengths = tokenized_inputs["attention_mask"].sum(1) max_input_length = input_lengths.max() input_ids = tokenized_inputs["input_ids"] all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1) top_n_tokens_tensor = torch.tensor( top_n_tokens, device=device, dtype=torch.int64 ) max_tokens = len(inputs) * (max_input_length + max_decode_tokens) return cls( batch_id=pb.id, requests=pb.requests, requests_idx_mapping=requests_idx_mapping, input_ids=input_ids, # past_input_ids=None, all_input_ids=list(all_input_ids), input_lengths=input_lengths.tolist(), prefix_offsets=prefix_offsets, read_offsets=read_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, max_input_length=max_input_length.item(), padding_right_offset=padding_right_offset, max_tokens=max_tokens, ) def filter(self, request_ids: List[int]) -> Optional["MambaBatch"]: if len(request_ids) == 0: raise ValueError("Batch must have at least one request") if len(request_ids) == len(self): return self keep_indices = [] # New values after filtering requests_idx_mapping = {} requests = [] input_lengths = [] prefix_offsets = [] read_offsets = [] all_input_ids = [] max_input_length = 0 next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] total_remaining_decode_tokens = 0 new_padding_right_offset = 0 indices = [] for i, request_id in enumerate(request_ids): idx = self.requests_idx_mapping[request_id] requests_idx_mapping[request_id] = i keep_indices.append(idx) requests.append(self.requests[idx]) prefix_offsets.append(self.prefix_offsets[idx]) read_offsets.append(self.read_offsets[idx]) all_input_ids.append(self.all_input_ids[idx]) request_input_length = self.input_lengths[idx] input_lengths.append(request_input_length) max_input_length = max(max_input_length, request_input_length) indices.append(idx) next_token_choosers.append(self.next_token_choosers[idx]) stopping_criteria = self.stopping_criterias[idx] stopping_criterias.append(stopping_criteria) top_n_tokens.append(self.top_n_tokens[idx]) remaining_decode_tokens = ( stopping_criteria.max_new_tokens - stopping_criteria.current_tokens ) total_remaining_decode_tokens += remaining_decode_tokens new_padding_right_offset = max( new_padding_right_offset, remaining_decode_tokens ) # Apply indices to input_ids, attention mask, past key values and other items that need to be cached input_ids = self.input_ids[keep_indices] top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices] max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens self.requests = requests self.requests_idx_mapping = requests_idx_mapping self.input_ids = input_ids self.all_input_ids = all_input_ids self.input_lengths = input_lengths self.prefix_offsets = prefix_offsets self.read_offsets = read_offsets self.next_token_choosers = next_token_choosers self.stopping_criterias = stopping_criterias self.top_n_tokens = top_n_tokens self.top_n_tokens_tensor = top_n_tokens_tensor self.max_input_length = max_input_length self.padding_right_offset = new_padding_right_offset self.max_tokens = max_tokens # TODO # Kept it simple by just updating the state, maybe updating the other CPU values is necessary. self.inference_params.conv_states = self.inference_params.conv_states[ :, indices ] self.inference_params.ssm_states = self.inference_params.ssm_states[:, indices] return self @classmethod def concatenate(cls, batches: List["MambaBatch"]) -> "MambaBatch": # Used for padding total_batch_size = 0 max_input_length = 0 padding_right_offset = 0 for batch in batches: total_batch_size += len(batch) max_input_length = max(max_input_length, batch.max_input_length) padding_right_offset = max(padding_right_offset, batch.padding_right_offset) # Batch attributes requests = [] requests_idx_mapping = {} input_lengths = [] prefix_offsets = [] read_offsets = [] all_input_ids = [] next_token_choosers = [] stopping_criterias = [] top_n_tokens = [] max_tokens = 0 seqlen_offset = 0 (n_blocks, _, d_inner, d_conv) = batches[0].inference_params.conv_states.shape (_, _, _, d_state) = batches[0].inference_params.ssm_states.shape dtype = batches[0].inference_params.conv_states.dtype device = batches[0].inference_params.conv_states.device inference_params = new_inference_params( n_blocks=n_blocks, batch_size=total_batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=device, dtype=dtype, ) # Batch tensors input_ids = None top_n_tokens_tensor = None # Used for slicing correctly inside the tensors # Equivalent to a cumsum on batch sizes start_index = 0 for i, batch in enumerate(batches): requests.extend(batch.requests) input_lengths.extend(batch.input_lengths) prefix_offsets.extend(batch.prefix_offsets) read_offsets.extend(batch.read_offsets) all_input_ids.extend(batch.all_input_ids) next_token_choosers.extend(batch.next_token_choosers) stopping_criterias.extend(batch.stopping_criterias) top_n_tokens.extend(batch.top_n_tokens) if i == 0: requests_idx_mapping = batch.requests_idx_mapping else: # We need to offset the mapping for each batch by the cumulative batch size for k, v in batch.requests_idx_mapping.items(): requests_idx_mapping[k] = v + start_index # Slicing end index for this batch end_index = start_index + len(batch) # Create empty tensor # input_ids is always of shape [batch_size, 1] # We do not need to pad it if input_ids is None: input_ids = batch.input_ids.new_empty((total_batch_size, 1)) # Copy to correct indices input_ids[start_index:end_index] = batch.input_ids if top_n_tokens_tensor is None: top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros( total_batch_size, ) top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor # Add eventual padding tokens that were added while concatenating max_tokens += batch.max_tokens + ( max_input_length - batch.max_input_length ) * len(batch) inference_params.max_seqlen = max( inference_params.max_seqlen, batch.inference_params.max_seqlen ) assert batch.inference_params.seqlen_offset != 0, "Invalid seqlen offset" inference_params.seqlen_offset = max( inference_params.seqlen_offset, batch.inference_params.seqlen_offset ) inference_params.conv_states[:, start_index:end_index] = ( batch.inference_params.conv_states ) inference_params.ssm_states[:, start_index:end_index] = ( batch.inference_params.ssm_states ) start_index = end_index return cls( batch_id=batches[0].batch_id, requests=requests, requests_idx_mapping=requests_idx_mapping, input_ids=input_ids, all_input_ids=all_input_ids, input_lengths=input_lengths, prefix_offsets=prefix_offsets, read_offsets=read_offsets, next_token_choosers=next_token_choosers, stopping_criterias=stopping_criterias, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, max_input_length=max_input_length, padding_right_offset=padding_right_offset, keys_head_dim_last=batches[0].keys_head_dim_last, max_tokens=max_tokens, inference_params=inference_params, ) def __len__(self): return len(self.requests) class Mamba(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, ): self.process_group, _rank, world_size = initialize_torch_distributed() if world_size > 1: raise RuntimeError("Mamba does not support Tensor Parallelism (TP)") self.cuda_graphs = {} if torch.cuda.is_available(): device = torch.device("cuda") # Bf16 is important. In f16 accumulations in the matmul are causing # differences while the server is under load. # This is detectable by the integration load test dtype = torch.bfloat16 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( "EleutherAI/gpt-neox-20b", revision=revision, padding_side="left", truncation_side="left", trust_remote_code=trust_remote_code, ) config = MambaConfig.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code ) tokenizer.bos_token_id = config.bos_token_id tokenizer.eos_token_id = config.eos_token_id tokenizer.pad_token = tokenizer.eos_token config.quantize = quantize config.speculator = speculator torch.distributed.barrier(group=self.process_group) weights_loader = get_loader( quantize=quantize, model_id=model_id, revision=revision ) filenames = weight_files(model_id, revision=revision, extension=".safetensors") weights = Weights( filenames, device, dtype, process_group=self.process_group, weights_loader=weights_loader, ) model = MambaModel(config, weights) torch.distributed.barrier(group=self.process_group) super(Mamba, self).__init__( model_id=model_id, model=model, tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, ) @property def batch_type(self) -> Type[MambaBatch]: return MambaBatch def warmup(self, batch) -> Optional[int]: # TODO: implement warmup for Mamba if needed if CUDA_GRAPHS: if self.speculate is None or self.speculate == 0: try: logger.info(f"Cuda Graphs are enabled for sizes {CUDA_GRAPHS}") # Warmup cuda graphs for bs in CUDA_GRAPHS: self.cuda_graph_warmup(bs) except Exception: logger.exception("Decode cuda graph warmup failed") else: logger.info(f"Cuda Graphs are disabled (CUDA_GRAPHS={CUDA_GRAPHS}).") return None def cuda_graph_warmup(self, batch_size: int): input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device) n_blocks = len(self.model.blocks) d_state = self.model.config.d_state d_conv = self.model.config.d_conv # Inner takes the expand multiplication d_inner = self.model.config.d_inner # Important seqlen_offset to go through the update mecanism with the state seqlen_offset = 1 inference_params = new_inference_params( n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype, ) graph = torch.cuda.CUDAGraph() torch.cuda.synchronize() # Run once outside to warmup self.model.forward(input_ids=input_ids, inference_params=inference_params) torch.cuda.synchronize() with torch.cuda.graph(graph, pool=MEM_POOL): logits, speculative_logits = self.model.forward( input_ids=input_ids, inference_params=inference_params ) torch.cuda.synchronize() graph_dict = { "input_ids": input_ids, "inference_params": inference_params, "graph": graph, "logits": logits, "speculative_logits": speculative_logits, } self.cuda_graphs[batch_size] = graph_dict def tunableop_warmup(self, batch_size: int, seqlen: int): input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device) n_blocks = len(self.model.blocks) d_state = self.model.config.d_state d_conv = self.model.config.d_conv # Inner takes the expand multiplication d_inner = self.model.config.d_inner # Important seqlen_offset to go through the update mecanism with the state seqlen_offset = 1 inference_params = new_inference_params( n_blocks=n_blocks, batch_size=seqlen, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype, ) self.model.forward(input_ids=input_ids, inference_params=inference_params) def forward( self, input_ids: torch.Tensor, inference_params: Any ) -> Tuple[torch.Tensor, torch.Tensor]: bs = input_ids.shape[0] padded_bs = bs if bs == 3: padded_bs = 4 elif 3 < bs <= 8: padded_bs = 8 elif bs > 8: padded_bs = (bs + 7) // 8 * 8 # Try to find an associated cuda graph cuda_graph = self.cuda_graphs.get(padded_bs, None) is_prefill = inference_params is None or inference_params.seqlen_offset == 0 if is_prefill or cuda_graph is None: return self.model( input_ids, inference_params=inference_params, ) # Copy inputs to the static inputs of the cuda graph # Static inputs are potentially padded cuda_graph["input_ids"][:bs] = input_ids cuda_graph["inference_params"].conv_states[ :, :bs ] = inference_params.conv_states cuda_graph["inference_params"].ssm_states[:, :bs] = inference_params.ssm_states # Replay the graph cuda_graph["graph"].replay() inference_params.conv_states.copy_( cuda_graph["inference_params"].conv_states[:, :bs] ) inference_params.ssm_states.copy_( cuda_graph["inference_params"].ssm_states[:, :bs] ) # Slice output to the correct shape speculative_logits = ( cuda_graph["speculative_logits"][:bs] if cuda_graph["speculative_logits"] is not None else None ) logits = cuda_graph["logits"][:bs] return logits, speculative_logits def generate_token(self, batch) -> Tuple[List[Any], Optional[Any], Tuple[int, int]]: start = time.time_ns() input_ids = ( batch.input_ids ) # batch.past_input_ids if batch.past_input_ids is not None else batch.input_ids batch_size, max_seqlen = input_ids.shape # Inference params if batch.inference_params is None: # 0 is important here seqlen_offset = 0 n_blocks = len(self.model.blocks) d_state = self.model.config.d_state d_conv = self.model.config.d_conv d_inner = self.model.config.d_inner inference_params = new_inference_params( n_blocks=n_blocks, batch_size=batch_size, d_state=d_state, d_conv=d_conv, d_inner=d_inner, seqlen_offset=seqlen_offset, device=self.device, dtype=self.dtype, ) batch.inference_params = inference_params # Forward pass logits, speculative_logits = self.forward( input_ids, inference_params=batch.inference_params ) # batch.inference_params = new_inference_params # Results generations: List[Generation] = [] stopped = True # Speculation is not active for causal accepted_ids = torch.ones_like(batch.input_ids)[:, 0] batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens( batch.top_n_tokens, batch.top_n_tokens_tensor, torch.log_softmax(logits[:, -1], -1), accepted_ids, ) start_decode = time.time_ns() # Zipped iterator iterator = zip( batch.requests, batch.input_lengths, batch.prefix_offsets, batch.read_offsets, logits, batch.next_token_choosers, batch.stopping_criterias, batch.all_input_ids, batch.top_n_tokens, batch_top_token_ids, batch_top_token_logprobs, ) # For each member of the batch for i, ( request, input_length, prefix_offset, read_offset, logits, next_token_chooser, stopping_criteria, all_input_ids, top_n_tokens, top_token_ids, top_token_logprobs, ) in enumerate(iterator): # Select next token next_token_id, logprobs = next_token_chooser( all_input_ids.view(1, -1), logits[-1:, :] ) # Append next token to all tokens all_input_ids = torch.cat([all_input_ids, next_token_id]) new_input_length = input_length + 1 # Generated token next_token_logprob = logprobs[-1, next_token_id] next_token_id_squeezed = next_token_id.squeeze() next_token_text, prefix_offset, read_offset = self.decode_token( all_input_ids[:, 0], prefix_offset, read_offset ) # Evaluate stopping criteria stop, reason = stopping_criteria( next_token_id_squeezed, next_token_text, ) if not stop: stopped = False # 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[:, 0], 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, ) # Get seed if isinstance(next_token_chooser.choice, Sampling): seed = next_token_chooser.choice.seed else: seed = None generated_text = GeneratedText( output_text, stopping_criteria.current_tokens, reason, seed ) else: generated_text = None if stopping_criteria.current_tokens == 1 and request.prefill_logprobs: # Remove generated token to only have prefill and add nan for first prompt token prefill_logprobs = [float("nan")] + torch.log_softmax( logits, -1 ).gather(1, all_input_ids[1:]).squeeze(1)[ -new_input_length:-1 ].tolist() prefill_token_ids = all_input_ids[-new_input_length:-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, prefill_logprobs, prefill_texts, is_special=[], ) else: prefill_tokens = None if top_n_tokens > 0: 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, ) else: top_tokens = None generation = Generation( request.id, prefill_tokens, Tokens( [next_token_id_squeezed], [next_token_logprob], [next_token_text], [next_token_id_squeezed.item() in self.all_special_ids], ), generated_text, top_tokens, ) generations.append(generation) # Update values batch.next_token_choosers[i] = batch.next_token_choosers[ i ].advance_grammar(next_token_id_squeezed.item()) batch.input_ids[i, 0] = next_token_id batch.all_input_ids[i] = all_input_ids batch.input_lengths[i] = new_input_length batch.prefix_offsets[i] = prefix_offset batch.read_offsets[i] = read_offset batch.max_input_length = max(batch.max_input_length, new_input_length) # We finished all generations in the batch; there is no next batch if stopped: forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, None, (forward_ns, decode_ns) # Slice unused values from prefill batch.input_ids = batch.input_ids[:, :1] forward_ns = start_decode - start decode_ns = time.time_ns() - start_decode return generations, batch, (forward_ns, decode_ns)