# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company. import bisect from dataclasses import dataclass from functools import wraps import itertools import math import os import tempfile from typing import Dict, List, Optional, Tuple, Type import torch from loguru import logger from opentelemetry import trace import text_generation_server.habana_quantization_env as hq_env import habana_frameworks.torch as htorch from habana_frameworks.torch.hpu import wrap_in_hpu_graph from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi from optimum.habana.utils import HabanaProfile from optimum.habana.transformers.generation import MODELS_OPTIMIZED_WITH_STATIC_SHAPES from optimum.habana.checkpoint_utils import ( get_repo_root, model_on_meta, write_checkpoints_json, ) from transformers import ( AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, AutoConfig, ) from text_generation_server.utils.tokens import batch_top_tokens from text_generation_server.models import Model 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 ( HeterogeneousNextTokenChooser, StoppingCriteria, make_tokenizer_optional, is_tokenizer_transparent, ) from text_generation_server.utils.debug import dbg_trace from text_generation_server.utils.speculate import get_speculate tracer = trace.get_tracer(__name__) MAX_TOTAL_TOKENS = int(os.environ.get('MAX_TOTAL_TOKENS', 2048)) BATCH_BUCKET_SIZE = int(os.environ.get('BATCH_BUCKET_SIZE', 8)) PAD_SEQUENCE_TO_MULTIPLE_OF = int(os.environ.get('PAD_SEQUENCE_TO_MULTIPLE_OF', 128)) PREFILL_BATCH_BUCKET_SIZE = int(os.environ.get('PREFILL_BATCH_BUCKET_SIZE', 4)) CHUNK_SIZES = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] def round_up(number, k): return (number + k - 1) // k * k def to_tensor_indices(indices, device): return torch.tensor(indices, dtype=torch.int32, device=device) def calculate_chunks(offset): result = [] while offset != 0: sign = 1 if offset > 0 else -1 best_chunk = min((abs(offset - sign * c), sign * c) for c in CHUNK_SIZES)[1] result.append(best_chunk) offset = offset - best_chunk return result def biggest_single_chunk(offset): if offset != 0: idx = bisect.bisect(CHUNK_SIZES, abs(offset)) return int(math.copysign(CHUNK_SIZES[idx - 1], offset)) else: return 0 def grouped_pad(tensor_groups, dims, values): grouped_result = [] for tensors, dim, value in zip(tensor_groups, dims, values): padding = MAX_TOTAL_TOKENS - tensors[0].size(dim) if dim is not None else 0 if padding > 0: assert dim in [-1, -2], f'Only dims -1 and -2 are supported! {dim}' pad_shape = (0, 0, 0, padding) if dim == -2 else (0, padding) result = [torch.nn.functional.pad(t, pad_shape, value=value) for t in tensors] else: result = [t for t in tensors] grouped_result.append(result) htorch.core.mark_step() return grouped_result def roll(tensor, chunk, dim, merge_graphs): if dim is None: return tensor tensor = torch.roll(tensor, chunk, dim) if not merge_graphs: htorch.core.mark_step() return tensor def grouped_roll(tensor_groups, chunk, dims, merge_graphs): tensor_groups = [[roll(t, chunk, dim, merge_graphs) for t in tensors] for tensors, dim in zip(tensor_groups, dims)] if merge_graphs: htorch.core.mark_step() return tensor_groups def grouped_shift(tensor_groups, dims, offset, merge_graphs): chunks = calculate_chunks(offset) for c in chunks: tensor_groups = grouped_roll(tensor_groups, c, dims, merge_graphs) return tensor_groups def move(dst_tensors, dst_indices, src_tensors): bs_dim = 0 num_indices = dst_indices.size(0) for i, (dst_t, src_t) in enumerate(zip(dst_tensors, src_tensors)): if src_t.size(bs_dim) != num_indices: src_t = torch.narrow(src_t, bs_dim, 0, num_indices) dst_t.index_copy_(bs_dim, dst_indices, src_t) htorch.core.mark_step() def grouped_move(dst_tensor_groups, dst_indices, src_tensor_groups): for dst_tensors, src_tensors in zip(dst_tensor_groups, src_tensor_groups): move(dst_tensors, dst_indices, src_tensors) def extend_tensor(tensor, padding, dim): result = torch.cat([tensor, padding], dim=dim) htorch.core.mark_step() return result def extend_batch(tensors, target_bs, dim): diff = target_bs - tensors[0].size(dim) # TODO: add support for shrinking bs if diff <= 0: return tensors shape = list(tensors[0].shape) shape[dim] = diff padding = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype) tensors = [extend_tensor(t, padding, dim) for t in tensors] return tensors def grouped_extend_batch(tensor_groups, target_bs, bs_dims): tensor_groups = [extend_batch(tensors, target_bs, dim) for tensors, dim in zip(tensor_groups, bs_dims)] return tensor_groups def merge(tensor_group): tensor_group = [torch.stack(tensor_group)] htorch.core.mark_step() return tensor_group def split(tensor_group, clone_data): tensor_group = [t.squeeze(0) for t in torch.split(tensor_group[0], 1)] if clone_data: tensor_group = [t.clone() for t in tensor_group] htorch.core.mark_step() return tensor_group def remove_kv_cache_from_output(module): orig_fwd = module.forward @wraps(orig_fwd) def forward(*args, **kwargs): if kwargs["past_key_values"] is not None: kwargs["return_dict"] = False output = orig_fwd(*args, **kwargs) first_value, second_value, *_ = output if first_value.nelement() < 2: return second_value else: return first_value else: kwargs["return_dict"] = True return orig_fwd(*args, **kwargs) module.forward = forward return module @dataclass class CausalLMRequest: idx: int data: generate_pb2.Request input_length: int prefix_offset: int read_offset: int stopping_criteria: StoppingCriteria all_input_ids: torch.Tensor @classmethod def from_pb(cls, idx: int, data: generate_pb2.Request, tokenizer: PreTrainedTokenizerBase): return cls( idx=idx, data=data, input_length=None, prefix_offset=None, read_offset=None, stopping_criteria=StoppingCriteria.from_pb(data.stopping_parameters, tokenizer), all_input_ids=None,) def update_idx(self, new_idx): prev = self.idx self.idx = new_idx return (new_idx, prev) @dataclass class CausalLMBatch(Batch): batch_id: int requests: List[CausalLMRequest] # Decoder values input_ids: torch.Tensor attention_mask: torch.Tensor position_ids: torch.Tensor past_key_values: Optional[List[Tuple]] merged_kv_cache: bool # Generation helpers next_token_chooser: HeterogeneousNextTokenChooser top_n_tokens: List[int] top_n_tokens_tensor: torch.Tensor input_length: int logits = None past = None keys_head_dim_last: bool = True def to_pb(self) -> generate_pb2.CachedBatch: return generate_pb2.CachedBatch( id=self.batch_id, request_ids=[r.data.id for r in self.requests], size=len(self), max_tokens=self.max_tokens, ) def detach_kv_cache(self): past_keys = [past[0] for past in self.past_key_values] past_values = [past[1] for past in self.past_key_values] del self.past_key_values return past_keys, past_values def attach_kv_cache(self, past_keys, past_values): # TODO: Add support for models that don't store kv_cache in a list self.past_key_values = list(zip(past_keys, past_values)) def merge_kv_cache_if_needed(self, target_bs, offset): pad_needed = self.seq_length < MAX_TOTAL_TOKENS shift_needed = offset != 0 expand_needed = target_bs > self.batch_size # Very simple heuristic to determine whether we should merge tensors # this needs tuning for other models/scenarios small_bs = len(self.past_key_values) > self.batch_size if not self.merged_kv_cache and small_bs and (pad_needed or shift_needed or expand_needed): past_keys, past_values = self.detach_kv_cache() past_keys = merge(past_keys) past_values = merge(past_values) self.attach_kv_cache(past_keys, past_values) self.merged_kv_cache = True def split_kv_cache_if_needed(self, clone_data): if self.merged_kv_cache: past_keys, past_values = self.detach_kv_cache() past_keys = split(past_keys, clone_data) past_values = split(past_values, clone_data) self.attach_kv_cache(past_keys, past_values) self.merged_kv_cache = False def get_tensor_groups(self): past_keys, past_values = self.detach_kv_cache() seq_dim = -1 key_dim = -2 if self.keys_head_dim_last else -1 value_dim = -2 tensors = [[self.input_ids], [self.attention_mask], [self.position_ids], past_keys, past_values] # We don't need to align position_ids seq_dims = [seq_dim, seq_dim, None, key_dim, value_dim] bs_dims = [0, 0, 0] + ([1, 1] if self.merged_kv_cache else [0, 0]) return tensors, seq_dims, bs_dims def set_tensor_groups(self, tensors): self.input_ids = tensors.pop(0)[0] self.attention_mask = tensors.pop(0)[0] self.position_ids = tensors.pop(0)[0] past_keys = tensors.pop(0) past_values = tensors.pop(0) self.attach_kv_cache(past_keys, past_values) def realign(self, target_bs, offset, pad_token_id): tensors, seq_dims, _ = self.get_tensor_groups() tensors = grouped_pad(tensors, seq_dims, [pad_token_id, 0, 0, 0, 0]) tensors = grouped_shift(tensors, seq_dims, offset, self.merged_kv_cache) self.set_tensor_groups(tensors) def expand_bs(self, target_bs): tensors, _, bs_dims = self.get_tensor_groups() tensors = grouped_extend_batch(tensors, target_bs, bs_dims) self.set_tensor_groups(tensors) def used_indices(self): return [req.idx for req in self.requests] def update_indices(self, new_indices): for req, new_idx in zip(self.requests, new_indices): req.idx = new_idx return self.used_indices() def free_indices_generator(self): used = set(req.idx for req in self.requests) return (i for i in range(self.batch_size) if i not in used) def move_data(self, src_batches): dst_tensors, _, dst_dims = self.get_tensor_groups() free_indices_gen = self.free_indices_generator() for src_b in src_batches: dst_indices = to_tensor_indices(src_b.update_indices(free_indices_gen), self.input_ids.device) src_tensors, _, src_dims = src_b.get_tensor_groups() grouped_move(dst_tensors, dst_indices, src_tensors) self.set_tensor_groups(dst_tensors) @classmethod def recombine(cls, batches: List["CausalLMBatch"], pad_token_id: int) -> "CausalLMBatch": if not all(b.past_key_values is not None for b in batches): raise ValueError("KV cache not allocated! Cannot recombine before prefill!") total_requests = sum(len(b) for b in batches) new_bs = round_up(total_requests, BATCH_BUCKET_SIZE) batch_id = batches[0].batch_id device = batches[0].input_ids.device input_lengths = [b.input_length for b in batches] max_input_length = max(input_lengths) offsets = [max_input_length - b.input_length for b in batches] cur_padding = [b.right_padding for b in batches] # For prefill there is a space allocated only for first token # Need to add padding to the max total tokens before first decode moves_needed = [total_requests - len(b) if b.batch_size == new_bs else total_requests for b in batches] dst_batch_idx = min(enumerate(moves_needed), key=lambda idx_val: idx_val[1])[0] reshape = (batches[dst_batch_idx].batch_size != new_bs) # TODO: Add support for changing max seq len, i.e. due to output length bucketing # FIXME: max_seq_len for non optimized code if len(batches) > 1: scenario = 'CONCAT' elif reshape: scenario = 'RESHAPE' elif cur_padding[dst_batch_idx] <= 0: scenario = 'SHIFT' offsets = [biggest_single_chunk(b.max_input_length - max_input_length) for b in batches] max_input_length = max_input_length + offsets[dst_batch_idx] else: # Nothing to do return batches[0] dbg_trace( scenario, f'bs:{[b.batch_size for b in batches]}->{new_bs}' f' reqs:{[len(b) for b in batches]}' f' offsets:{offsets}' f' input_lengths:{input_lengths}' f' cur_padding:{cur_padding}' f' dst_batch:{dst_batch_idx}') grouped_requests = [[req for req in batch.requests] for batch in batches] flat_requests = list(itertools.chain(*grouped_requests)) for i in range(len(batches)): target_bs = new_bs if i == dst_batch_idx else batches[i].batch_size batches[i].merge_kv_cache_if_needed(target_bs, offsets[i]) batches[i].realign(target_bs, offsets[i], pad_token_id) batches[i].split_kv_cache_if_needed(i == dst_batch_idx) batches[dst_batch_idx].expand_bs(new_bs) batches[dst_batch_idx].move_data([batches[i] for i in range(len(batches)) if i != dst_batch_idx]) top_n_tokens = [r.data.top_n_tokens for r in flat_requests] top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64) parameters = [r.data.parameters for r in flat_requests] if len(flat_requests) < new_bs: for i in range(new_bs-len(flat_requests)) : # append the dummy parameters for dummy request parameters.append(parameters[0]) next_token_chooser = HeterogeneousNextTokenChooser.from_pb( parameters, batches[dst_batch_idx].next_token_chooser.dtype, batches[dst_batch_idx].next_token_chooser.device, hq_env.is_quantization_enabled ) input_ids = batches[dst_batch_idx].input_ids attention_mask = batches[dst_batch_idx].attention_mask position_ids = batches[dst_batch_idx].position_ids past_key_values = batches[dst_batch_idx].past_key_values input_length = max_input_length htorch.core.mark_step() return cls( batch_id=batch_id, requests=flat_requests, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, merged_kv_cache=False, next_token_chooser=next_token_chooser, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, input_length=input_length, ) @classmethod def from_pb( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, ) -> "CausalLMBatch": dbg_trace('FROM_PB', f'num_reqs:{len(pb.requests)}') requests = [CausalLMRequest.from_pb(idx, req, tokenizer) for idx, req in enumerate(pb.requests)] max_input_length = max(r.data.truncate for r in requests) max_new_tokens = max(r.stopping_criteria.max_new_tokens for r in requests) # TODO: Add support for sparse batches top_n_tokens = [r.top_n_tokens for r in pb.requests] top_n_tokens_tensor = torch.tensor(top_n_tokens, device=device, dtype=torch.int64) # TODO: by tokenizing all inputs at once we loose information on actual input lengths # this means that we cannot shift inputs to the left after a long input sequence # was filtered out new_bs = round_up(len(requests), PREFILL_BATCH_BUCKET_SIZE) dummy_inputs = ["?"] * (new_bs - len(requests)) parameters = [r.parameters for r in pb.requests] if len(pb.requests) < new_bs: for i in range(new_bs-len(pb.requests)) : #append the dummy parameters for dummy request parameters.append(parameters[0]) next_token_chooser = HeterogeneousNextTokenChooser.from_pb( parameters, dtype, device, hq_env.is_quantization_enabled ) tokenized_inputs = tokenizer( [r.data.inputs for r in requests] + dummy_inputs, return_tensors="pt", padding="longest", return_token_type_ids=False, truncation=True, max_length=max_input_length, ) input_len = tokenized_inputs["input_ids"].shape[1] bucket_size = max_input_length left_padding = max_input_length - input_len if input_len < max_input_length and PAD_SEQUENCE_TO_MULTIPLE_OF != 0: assert PAD_SEQUENCE_TO_MULTIPLE_OF <= max_input_length, "PAD_SEQUENCE_TO_MULTIPLE_OF cannot be higher than max_input_length" rounded_seq_len = round_up(input_len + 1, PAD_SEQUENCE_TO_MULTIPLE_OF) if rounded_seq_len <= max_input_length: bucket_size = rounded_seq_len - 1 else: bucket_size = max_input_length - 1 left_padding = bucket_size - input_len input_ids = tokenized_inputs["input_ids"] attention_mask = tokenized_inputs["attention_mask"] # Allocate space for first token input_ids = torch.nn.functional.pad( input_ids, (left_padding, 1), value=tokenizer.pad_token_id ) attention_mask = torch.nn.functional.pad( attention_mask, (left_padding, 1), value=0 ) all_input_ids = torch.nn.functional.pad( input_ids, (0, max_new_tokens), value=tokenizer.pad_token_id ).T.split(1, dim=1) # New input length after left padding input_len = bucket_size for r in requests: r.input_length = input_len r.prefix_offset = input_len - 5 r.read_offset = input_len r.all_input_ids = all_input_ids[r.idx] input_ids = input_ids.to(device) attention_mask = attention_mask.to(device) position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) htorch.core.mark_step() return cls( batch_id=pb.id, requests=requests, input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=None, merged_kv_cache=False, next_token_chooser=next_token_chooser, top_n_tokens=top_n_tokens, top_n_tokens_tensor=top_n_tokens_tensor, input_length=input_len, ) @tracer.start_as_current_span("filter") def filter(self, request_ids: List[int]) -> 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 @classmethod @tracer.start_as_current_span("concatenate") 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) @property def max_input_length(self): return max(req.input_length for req in self.requests) @property def batch_size(self): return self.attention_mask.size(0) @property def seq_length(self): return self.attention_mask.size(1) @property def right_padding(self): return self.seq_length - self.input_length # Maximum number of tokens this batch will grow to @property def max_tokens(self): max_total_tokens = self.attention_mask.size(1) return len(self.requests) * max_total_tokens class CausalLM(Model): def __init__( self, model_id: str, revision: Optional[str] = None, dtype: Optional[torch.dtype] = None, ): adapt_transformers_to_gaudi() # Create tokenizer tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", truncation_side="left", ) make_tokenizer_optional(tokenizer) # Create model world_size = int(os.getenv("WORLD_SIZE", "1")) rank = int(os.getenv("RANK", "0")) dtype = torch.bfloat16 if dtype is None else dtype device = torch.device("hpu") if hq_env.is_quantization_enabled: htorch.core.hpu_set_env() if world_size > 1: model = self.get_deepspeed_model( model_id, dtype, revision ) model = self.prepare_model_for_quantization(model) else: get_repo_root(model_id) # Check support for rope scaling model_kwargs = {} config = AutoConfig.from_pretrained( model_id ) if hasattr(config, "rope_scaling"): model_kwargs["rope_scaling"] = self.get_rope_scaling() model = AutoModelForCausalLM.from_pretrained( model_id, revision=revision, torch_dtype=dtype, **model_kwargs ) model = self.prepare_model_for_quantization(model) model = model.eval().to(device) self.enable_hpu_graph = os.getenv("ENABLE_HPU_GRAPH", "true").lower() == "true" self.limit_hpu_graph = os.getenv("LIMIT_HPU_GRAPH", "false").lower() == "true" model = remove_kv_cache_from_output(model) if self.enable_hpu_graph: model = wrap_in_hpu_graph(model, disable_tensor_cache=True) model = self.setup_quantization(model) if model.config.model_type not in MODELS_OPTIMIZED_WITH_STATIC_SHAPES: raise ValueError(f"Model type {model.config.model_type} is not supported!") 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]"}) kwargs = { "use_cache": True, "return_dict": True, } if model.config.model_type == "llama": kwargs["attn_softmax_bf16"] = True kwargs["trim_logits"] = True self.speculate = get_speculate() super(CausalLM, self).__init__( model=model, tokenizer=tokenizer, requires_padding=True, dtype=dtype, device=device, rank=rank, kwargs=kwargs, ) # Create profiler ranks_to_profile = [int(val) for val in os.getenv("PROF_RANKS", "0").split(',')] record_shapes = os.getenv("PROF_RECORD_SHAPES", "false").lower() == "true" output_dir = os.getenv("PROF_PATH", "/tmp/hpu_profile") self.profiling_warmup_steps = int(os.getenv("PROF_WARMUPSTEP", "0")) if rank in ranks_to_profile else 0 self.profiling_steps = int(os.getenv("PROF_STEP", "0")) if rank in ranks_to_profile else 0 self.profiling_wait_steps = int(os.getenv("PROF_WAITSTEP", "0")) if self.profiling_steps > 0: self.hb_profiler = HabanaProfile( wait=self.profiling_wait_steps, warmup=self.profiling_warmup_steps, active=self.profiling_steps, output_dir=output_dir, record_shapes=record_shapes ) self.hb_profiler.start() else: self.hb_profiler = None self.step = 0 def get_deepspeed_model( self, model_id: str, dtype: torch.dtype, revision: Optional[str] = None ) -> torch.nn.Module: import deepspeed from habana_frameworks.torch.distributed.hccl import initialize_distributed_hpu world_size, rank, local_rank = initialize_distributed_hpu() model_kwargs = { "revision": revision } # Initialize process(es) for DeepSpeed deepspeed.init_distributed(dist_backend="hccl") logger.info( "DeepSpeed is enabled. world_size {} rank {} local_rank {}".format(world_size, rank, local_rank) ) config = AutoConfig.from_pretrained(model_id, **model_kwargs) load_to_meta = model_on_meta(config) # Check support for rope scaling if hasattr(config, "rope_scaling"): config.rope_scaling = self.get_rope_scaling() model_kwargs["rope_scaling"] = self.get_rope_scaling() if load_to_meta: # Construct model with fake meta tensors, later will be replaced on devices during ds-inference ckpt load with deepspeed.OnDevice(dtype=dtype, device="meta"): model = AutoModelForCausalLM.from_config(config, torch_dtype=dtype) else: get_repo_root(model_id, local_rank=os.getenv("LOCAL_RANK")) # TODO: revisit placement on CPU when auto-injection is possible with deepspeed.OnDevice(dtype=dtype, device="cpu"): model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, **model_kwargs) model = model.eval() # Initialize the model ds_inference_kwargs = {"dtype": dtype} ds_inference_kwargs["tensor_parallel"] = {"tp_size": world_size} ds_inference_kwargs["enable_cuda_graph"] = False if load_to_meta: # model loaded to meta is managed differently checkpoints_json = tempfile.NamedTemporaryFile(suffix=".json", mode="+w") write_checkpoints_json(model_id, local_rank, checkpoints_json) ds_inference_kwargs["checkpoint"] = checkpoints_json.name model = deepspeed.init_inference(model, **ds_inference_kwargs) return model.module def get_rope_scaling(self) -> Optional[Dict]: rope_scaling = os.getenv("ROPE_SCALING", None) if rope_scaling is None: return None rope_factor = float(os.getenv("ROPE_FACTOR", 1.0)) return { 'type': rope_scaling, 'factor': float(rope_factor) } def setup_quantization(self, model): if hq_env.is_quantization_enabled: htorch.core.quantization._mark_params_as_const(model) htorch.core.quantization._check_params_as_const(model) htorch.core.hpu_initialize(model) return model def prepare_model_for_quantization(self, model): if hq_env.is_quantization_enabled: if model.config.model_type == "llama": self.patch_scoped_linear_all_reduce(model) import habana_quantization_toolkit habana_quantization_toolkit.prep_model(model) return model def finish_quantization_measurements(self, model): if hq_env.is_quantization_enabled: import habana_quantization_toolkit habana_quantization_toolkit.finish_measurements(self.model) return model def patch_scoped_linear_all_reduce(self, model): from deepspeed.module_inject.layers import LinearAllreduce from optimum.habana.transformers.models.modeling_all_models import ScopedLinearAllReduce for name, module in model.named_children(): if type(module) is LinearAllreduce: SL = ScopedLinearAllReduce(mod=module) setattr(model, name, SL) self.patch_scoped_linear_all_reduce(module) @property def batch_type(self) -> Type[CausalLMBatch]: return CausalLMBatch def decode(self, generated_ids: List[int]) -> str: return self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False) def decode_token( self, all_input_ids: List[int], prefix_offset: int = 0, read_offset: int = 0, ) -> Tuple[str, int, int]: if is_tokenizer_transparent(self.tokenizer): new_text = self.tokenizer.decode(all_input_ids[read_offset:], skip_special_tokens=False) return new_text, read_offset, len(all_input_ids) else: return super().decode_token(all_input_ids, prefix_offset, read_offset) def forward( self, input_ids, attention_mask, position_ids, token_idx, past_key_values: Optional = None, bypass_hpu_graph: Optional = None, ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]: # Model Forward kwargs = { "input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values, "token_idx": token_idx } if self.has_position_ids: kwargs["position_ids"] = position_ids if bypass_hpu_graph != None: kwargs["bypass_hpu_graphs"] = bypass_hpu_graph kwargs.update(self.kwargs) if past_key_values is not None: return self.model.forward(**kwargs) else: outputs = self.model.forward(**kwargs) return outputs.logits, outputs.past_key_values @tracer.start_as_current_span("generate_token") def generate_token(self, batches: List[CausalLMBatch]) -> Tuple[List[Generation], Optional[CausalLMBatch]]: # Results generations: List[Generation] = [] prev_batches = [] requests_to_generate = [] # In order to pipeline any actions on CPU we perform the operation in 3 main stages: # Stage 1. Collect next token ids of any previously started generations for batch_id, batch in enumerate(batches): if batch.logits is not None: logits = batch.logits past = batch.past prefill = batch.past_key_values is None if prefill: # no right padding for prefill token_idx_scalar = batch.attention_mask.shape[-1] - 1 token_idx = torch.tensor(token_idx_scalar).to(self.device) else: token_idx_scalar = batch.attention_mask.shape[-1] - batch.right_padding token_idx = torch.tensor(token_idx_scalar).to(self.device) # Select next token input_length = batch.input_length if logits.shape[-2] > 1: next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser( batch.input_ids, logits[:, input_length - 1: input_length, :].squeeze(-2), self.speculate ) else: next_token_ids, next_token_logprobs, logprobs, _, _ = batch.next_token_chooser( batch.input_ids, logits.squeeze(-2), self.speculate ) batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens( batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs, ) prev_batches.append({ 'next_token_ids': next_token_ids, 'next_token_logprobs': next_token_logprobs, }) for req_idx, req in enumerate(batch.requests): requests_to_generate.append({ 'req': req, 'prev_req_idx': req.idx, 'batch_id': batch_id, 'seed': batch.next_token_chooser.seeds[req_idx], 'do_sample': batch.next_token_chooser.do_sample[req_idx], 'top_n_tokens': batch.top_n_tokens[req_idx], 'top_token_ids': batch_top_token_ids[req_idx], 'top_token_logprobs': batch_top_token_logprobs[req_idx], }) htorch.core.mark_step() # Add new token into input_ids batch.input_ids.index_copy_(1, token_idx, next_token_ids.unsqueeze(1)) # Update attention_mask as we added a new token to input_ids batch.attention_mask.index_fill_(1, token_idx, 1) # Adjust lengths batch.input_length += 1 # Update position_ids if prefill: batch.position_ids = torch.index_select(batch.position_ids, 1, token_idx - 1) + 1 else: batch.position_ids += 1 # Update past key values if prefill: batch.past_key_values = past htorch.core.mark_step() # Stage 2. Prepare new batch for speculative scheduling if len(batches) > 1: batch = self.batch_type.concatenate(batches, self.tokenizer.pad_token_id) else: batch = batches[0] 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.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} padding:{batch.right_padding}') assert batch.right_padding > 0, 'No more room for next token!' # Execute batch if prefill: # no right padding for prefill token_idx = torch.tensor(batch.attention_mask.shape[-1] - 1).to(self.device) batch.logits, batch.past = self.forward( batch.input_ids, batch.attention_mask, batch.position_ids, token_idx, batch.past_key_values, bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None ) else: token_idx = torch.tensor(batch.attention_mask.shape[-1] - batch.right_padding).to(self.device) input_ids = torch.index_select(batch.input_ids, 1, token_idx - 1) batch.logits = self.forward( input_ids, batch.attention_mask, batch.position_ids, token_idx, batch.past_key_values, bypass_hpu_graph=prefill and self.limit_hpu_graph if self.enable_hpu_graph else None ) htorch.core.mark_step() # Stage 3. Finish and return previous generations stopped = len(requests_to_generate) > 0 for prev_batch in prev_batches: prev_batch['next_token_logprobs'] = prev_batch['next_token_logprobs'].tolist() prev_batch['next_token_ids_cpu'] = prev_batch['next_token_ids'].cpu() htorch.core.mark_step() for req_data in requests_to_generate: req = req_data['req'] i = req_data['prev_req_idx'] prev_batch_id = req_data['batch_id'] assert len(prev_batches) > prev_batch_id next_token_ids_cpu = prev_batches[prev_batch_id]['next_token_ids_cpu'] next_token_logprobs = prev_batches[prev_batch_id]['next_token_logprobs'] request = req.data input_length = req.input_length prefix_offset = req.prefix_offset read_offset = req.read_offset do_sample = req_data['do_sample'] seed = req_data['seed'] stopping_criteria = req.stopping_criteria all_input_ids = req.all_input_ids next_token_id = next_token_ids_cpu[i] next_token_logprob = next_token_logprobs[i] top_n_tokens = req_data['top_n_tokens'] top_token_ids = req_data['top_token_ids'] top_token_logprobs = req_data['top_token_logprobs'] # Append next token to all tokens all_input_ids[input_length] = next_token_id new_input_length = input_length + 1 # Generated token if is_tokenizer_transparent(self.tokenizer) and len(stopping_criteria.stop_sequence_criterias) == 0: next_token_text = '' else: next_token_text, prefix_offset, read_offset = self.decode_token( all_input_ids[0:new_input_length, 0], prefix_offset, read_offset ) # Evaluate stopping criteria stop, reason = stopping_criteria( next_token_id, 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 if is_tokenizer_transparent(self.tokenizer): output_text = None else: output_text = self.decode( all_input_ids[new_input_length - stopping_criteria.current_tokens: new_input_length, 0] ) generated_text = GeneratedText( output_text, stopping_criteria.current_tokens, reason, seed if do_sample else None, ) else: generated_text = None # Prefill 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")] + next_token_logprobs prefill_token_ids = all_input_ids[0: 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], [next_token_logprob], [next_token_text], [next_token_id in self.all_special_ids], ), generated_text, top_tokens, ) generations.append(generation) req.all_input_ids = all_input_ids req.input_length = new_input_length req.prefix_offset = prefix_offset req.read_offset = read_offset htorch.core.mark_step() self.step = self.step + 1 if self.hb_profiler is not None: if self.step > self.profiling_wait_steps + self.profiling_warmup_steps + self.profiling_steps: self.hb_profiler.stop() else: self.hb_profiler.step() return generations, batch if not stopped else None def warmup(self, batches: List[CausalLMBatch]) -> None: # prefill _, prefill_batch = self.generate_token([batches.pop(0)]) # decode _, decode_batch = self.generate_token([prefill_batch]) # shifts self.shifting_warmup(decode_batch) # if decode bs is 1 warmup ends here if len(batches) == 0: return # prefill _, prefill_batch = self.generate_token([batches.pop(0)]) # concatenate and decode _, decode_batch = self.generate_token([decode_batch, prefill_batch]) # decodes while decode_batch is not None: _, decode_batch = self.generate_token([decode_batch]) def shifting_warmup(self, batch: CausalLMBatch) -> None: chunk_sizes = CHUNK_SIZES.copy() chunk_sizes.extend([-chunk for chunk in chunk_sizes]) for chunk in chunk_sizes: batch.merge_kv_cache_if_needed(batch.batch_size, chunk) batch.realign(batch.batch_size, chunk, 0) batch.split_kv_cache_if_needed(True)