mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-06-19 07:42:06 +00:00
Remove Optimum-habana
Signed-off-by: yuanwu <yuan.wu@intel.com>
This commit is contained in:
yuanwu
parent
1ff9d185d5
commit
c065c58818
@ -22,10 +22,9 @@ opentelemetry-instrumentation-grpc = "^0.53b0"
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hf-transfer = "^0.1.9"
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sentencepiece = "^0.2.0"
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peft = "^0.15"
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optimum-habana = "1.17"
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transformers = "^4.49"
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transformers = "^4.52.4"
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numpy = "^1.26"
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accelerate = "^0.33"
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accelerate = "^1.7.0"
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outlines= { version = "^0.0.36", optional = true }
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prometheus-client = "^0.21.1"
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py-cpuinfo = "^9.0.0"
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@ -1,4 +1,4 @@
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accelerate==0.33.0 ; python_version >= "3.9" and python_version < "3.13"
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accelerate==1.7.0 ; python_version >= "3.9" and python_version < "3.13"
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annotated-types==0.7.0 ; python_version >= "3.9" and python_version < "3.13"
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attrs==25.3.0 ; python_version >= "3.9" and python_version < "3.13"
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certifi==2025.1.31 ; python_version >= "3.9" and python_version < "3.13"
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@ -46,7 +46,6 @@ opentelemetry-instrumentation==0.53b0 ; python_version >= "3.9" and python_versi
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opentelemetry-proto==1.32.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-sdk==1.32.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-semantic-conventions==0.53b0 ; python_version >= "3.9" and python_version < "3.13"
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optimum-habana==1.17.0 ; python_version >= "3.9" and python_version < "3.13"
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optimum==1.24.0 ; python_version >= "3.9" and python_version < "3.13"
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outlines==0.0.36 ; python_version >= "3.9" and python_version < "3.13"
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packaging==24.2 ; python_version >= "3.9" and python_version < "3.13"
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@ -76,7 +75,7 @@ sympy==1.13.1 ; python_version >= "3.9" and python_version < "3.13"
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threadpoolctl==3.6.0 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.21.1 ; python_version >= "3.9" and python_version < "3.13"
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tqdm==4.67.1 ; python_version >= "3.9" and python_version < "3.13"
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transformers==4.49.0 ; python_version >= "3.9" and python_version < "3.13"
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transformers==4.52.4 ; python_version >= "3.9" and python_version < "3.13"
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triton==3.2.0 ; python_version >= "3.9" and python_version < "3.13" and platform_system == "Linux" and platform_machine == "x86_64"
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typer==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
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typing-extensions==4.13.2 ; python_version >= "3.9" and python_version < "3.13"
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@ -5,7 +5,6 @@ import os
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from loguru import logger
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from transformers.configuration_utils import PretrainedConfig
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from transformers.models.auto import modeling_auto
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from huggingface_hub import hf_hub_download, HfApi
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from typing import Optional
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from pathlib import Path
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@ -882,72 +881,6 @@ def get_model(
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trust_remote_code=trust_remote_code,
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)
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from text_generation_server.models.causal_lm import CausalLM
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from text_generation_server.models.vlm_causal_lm import VlmCausalLM
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from text_generation_server.models.custom_modeling.mllama import (
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MllamaForConditionalGeneration,
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)
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from text_generation_server.models.custom_modeling.llava_next import (
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LlavaNextForConditionalGeneration,
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)
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from text_generation_server.models.vlm_causal_lm import (
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VlmCausalLMBatch,
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)
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VLM_BATCH_TYPES.add(VlmCausalLMBatch)
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from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
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adapt_transformers_to_gaudi()
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if SDP_ON_BF16 == 1:
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torch._C._set_math_sdp_allow_fp16_bf16_reduction(True)
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if model_type == "gpt_bigcode":
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from text_generation_server.models.starcoder import StarCoder
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return StarCoder(model_id=model_id, revision=revision, dtype=dtype)
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if model_type == "bloom":
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from text_generation_server.models.bloom import BLOOM
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return BLOOM(
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model_id=model_id,
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revision=revision,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type == "llava_next":
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return VlmCausalLM(
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model_class=LlavaNextForConditionalGeneration,
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model_id=model_id,
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revision=revision,
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quantize=None,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type == "mllama":
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return VlmCausalLM(
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model_class=MllamaForConditionalGeneration,
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model_id=model_id,
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revision=revision,
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quantize=None,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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raise ValueError(f"Unsupported model type {model_type}")
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@ -1,52 +0,0 @@
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# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
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import torch
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from typing import Optional, Type
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from transformers import PreTrainedTokenizerBase
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from text_generation_server.models import CausalLM
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from text_generation_server.models.causal_lm import CausalLMBatch
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from text_generation_server.pb import generate_pb2
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class BloomCausalLMBatch(CausalLMBatch):
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "CausalLMBatch":
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batch = super().from_pb(
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pb=pb,
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tokenizer=tokenizer,
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dtype=dtype,
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device=device,
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)
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batch.keys_head_dim_last = False
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return batch
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class BLOOM(CausalLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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speculator: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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super(BLOOM, self).__init__(
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model_id=model_id,
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revision=revision,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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@property
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def batch_type(self) -> Type[CausalLMBatch]:
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return BloomCausalLMBatch
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File diff suppressed because it is too large
Load Diff
@ -49,7 +49,8 @@ from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
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# Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
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from typing import Union
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.image_utils import ImageInput, VideoInput
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from transformers.image_utils import ImageInput
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from transformers.video_utils import VideoInput
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from transformers.processing_utils import (
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ProcessingKwargs,
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ProcessorMixin,
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@ -1,156 +0,0 @@
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import re
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import torch
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import torch.distributed
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from transformers import (
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PreTrainedTokenizerBase,
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)
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from text_generation_server.models.causal_lm import CausalLMBatch
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import (
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NextTokenChooser,
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StoppingCriteria,
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)
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from text_generation_server.utils.chunks import concat_text_chunks
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# CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
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# we split individual characters inside special tokens like [START_DNA]
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CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
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# token added to implement a custom sequence tokenization. This token is added at
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# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
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# that they do not occur in the corpus. The digits are escaped so that the token does not appear
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# literally in the source code in case we ever include it in the training data.
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SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
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def _insert_split_marker(m: re.Match):
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"""
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Applies split marker based on a regex match of special tokens such as
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[START_DNA].
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Parameters
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----------
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n : str
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Input text to split
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Returns
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----------
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str - the text with the split token added
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"""
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start_token, _, sequence, end_token = m.groups()
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sequence = re.sub(r"(.)", rf"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
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return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
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def escape_custom_split_sequence(text):
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"""
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Applies custom splitting to the text for GALILEO's tokenization
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Parameters
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----------
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text : str
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Input text to split
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Returns
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----------
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str - the text with the split token added
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"""
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return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
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# END CREDIT
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class GalacticaCausalLMBatch(CausalLMBatch):
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "GalacticaCausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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top_n_tokens = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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max_truncation = 0
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padding_right_offset = 0
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max_decode_tokens = 0
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for i, r in enumerate(pb.requests):
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requests_idx_mapping[r.id] = i
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# Add escape_custom_split_sequence to the CausalLMBatch logic
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inputs.append(
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escape_custom_split_sequence(concat_text_chunks(r.input_chunks.chunks))
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)
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next_token_choosers.append(
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NextTokenChooser.from_pb(r.parameters, device, tokenizer)
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)
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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top_n_tokens.append(r.top_n_tokens)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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tokenized_inputs = tokenizer(
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inputs,
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return_tensors="pt",
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padding=True,
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return_token_type_ids=False,
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truncation=True,
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max_length=max_truncation,
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(0)
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read_offsets.append(input_len)
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
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top_n_tokens_tensor = torch.tensor(
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top_n_tokens, device=device, dtype=torch.int64
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)
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max_tokens = len(inputs) * max_input_length + max_decode_tokens
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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top_n_tokens=top_n_tokens,
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top_n_tokens_tensor=top_n_tokens_tensor,
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max_input_length=max_input_length.item(),
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padding_right_offset=padding_right_offset,
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max_tokens=max_tokens,
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)
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@ -1,882 +0,0 @@
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from io import BytesIO
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from PIL import Image
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import torch
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import time
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import (
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AutoConfig,
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AutoProcessor,
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AutoTokenizer,
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PreTrainedTokenizerBase,
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ProcessorMixin,
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)
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from typing import Optional, Tuple, List, Type, Dict
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from text_generation_server.models import Model
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from text_generation_server.models.types import (
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Batch,
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Tokens,
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Generation,
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GeneratedText,
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)
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
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import torch.distributed
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from text_generation_server.models.custom_modeling.idefics_modeling import (
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IdeficsForVisionText2Text,
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)
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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from text_generation_server.utils.quantization import get_loader
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tracer = trace.get_tracer(__name__)
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@dataclass
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class IdeficsCausalLMBatch(Batch):
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batch_id: int
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requests: List[generate_pb2.Request]
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requests_idx_mapping: Dict[int, int]
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# Decoder values
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input_ids: torch.Tensor
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attention_mask: torch.Tensor
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position_ids: torch.Tensor
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pixel_values: Optional[torch.Tensor]
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image_hidden_states: Optional[torch.Tensor]
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image_attention_mask: Optional[torch.Tensor]
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past_key_values: Optional[List[Tuple]]
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# All tokens
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all_input_ids: List[torch.Tensor]
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# Lengths of all generations present in the batch
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input_lengths: List[int]
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prefix_offsets: List[int]
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read_offsets: List[int]
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# Generation helpers
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next_token_choosers: List[NextTokenChooser]
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stopping_criterias: List[StoppingCriteria]
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# Metadata used for padding
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max_input_length: int
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padding_right_offset: int
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# Maximum number of tokens this batch will grow to
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max_tokens: int
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# Past metadata
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keys_head_dim_last: bool = True
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def to_pb(self) -> generate_pb2.CachedBatch:
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return generate_pb2.CachedBatch(
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id=self.batch_id,
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request_ids=[r.id for r in self.requests],
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size=len(self),
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max_tokens=self.max_tokens,
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)
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "IdeficsCausalLMBatch":
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raise NotImplementedError
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@classmethod
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def from_pb_processor(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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processor: ProcessorMixin, # Hack
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config,
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dtype: torch.dtype,
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device: torch.device,
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) -> "IdeficsCausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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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(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)
|
||||
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
|
||||
)
|
||||
|
||||
# TODO Check impact on idefics
|
||||
prompts = []
|
||||
for inp in inputs:
|
||||
# Each input is encoded into a list, where each element of this input list is either a string or a URL
|
||||
prompt = []
|
||||
for chunk in inp:
|
||||
chunk_type = chunk.WhichOneof("chunk")
|
||||
if chunk_type == "text":
|
||||
prompt.append(chunk.text)
|
||||
elif chunk_type == "image":
|
||||
image = Image.open(BytesIO(chunk.image.data))
|
||||
prompt.append(image)
|
||||
else:
|
||||
raise RuntimeError(f"Invalid chunk type {chunk_type}")
|
||||
prompts.append(prompt)
|
||||
|
||||
# The processor replaces the call to tokenizer, and
|
||||
# a/ takes care of fetching images from the URL
|
||||
# b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
|
||||
tokenized_inputs = processor(
|
||||
prompts,
|
||||
return_tensors="pt",
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
# TODO Check impact on idefics
|
||||
# add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
|
||||
).to(device)
|
||||
for _ in pb.requests:
|
||||
input_len = tokenized_inputs["input_ids"].shape[1]
|
||||
prefix_offsets.append(
|
||||
input_len - 5
|
||||
) # To decode without potential fallbacks errors
|
||||
read_offsets.append(
|
||||
input_len
|
||||
) # To decode without potential fallbacks errors
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
max_input_length = input_lengths.max()
|
||||
|
||||
input_ids = tokenized_inputs["input_ids"]
|
||||
pixel_values = tokenized_inputs.get("pixel_values", None)
|
||||
image_hidden_states = None
|
||||
# Allocate maximum attention_mask
|
||||
attention_mask = input_ids.new_zeros(
|
||||
(pb.size, max_input_length + padding_right_offset)
|
||||
)
|
||||
# Copy tokenizer attention_mask into fully allocated attention_mask
|
||||
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
|
||||
# Do the same for image_attention_mask
|
||||
if pixel_values is None:
|
||||
image_attention_mask = None
|
||||
else:
|
||||
image_attention_mask = input_ids.new_zeros(
|
||||
(
|
||||
pb.size,
|
||||
max_input_length + padding_right_offset,
|
||||
pixel_values.size(1),
|
||||
)
|
||||
)
|
||||
image_attention_mask[:, :max_input_length, :] = tokenized_inputs[
|
||||
"image_attention_mask"
|
||||
]
|
||||
|
||||
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
|
||||
all_input_ids = tokenized_inputs["input_ids"].T.split(
|
||||
1, dim=1
|
||||
) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
|
||||
|
||||
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,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
pixel_values=pixel_values,
|
||||
image_hidden_states=image_hidden_states,
|
||||
image_attention_mask=image_attention_mask,
|
||||
past_key_values=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,
|
||||
max_input_length=max_input_length.item(),
|
||||
padding_right_offset=padding_right_offset,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("filter")
|
||||
def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
|
||||
# It deletes requests from the batch. For instance when client lost connection
|
||||
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 = []
|
||||
|
||||
total_remaining_decode_tokens = 0
|
||||
new_padding_right_offset = 0
|
||||
|
||||
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)
|
||||
|
||||
next_token_choosers.append(self.next_token_choosers[idx])
|
||||
stopping_criteria = self.stopping_criterias[idx]
|
||||
stopping_criterias.append(stopping_criteria)
|
||||
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]
|
||||
position_ids = self.position_ids[keep_indices]
|
||||
self.attention_mask = self.attention_mask[
|
||||
keep_indices,
|
||||
-(self.padding_right_offset + max_input_length) : (
|
||||
self.attention_mask.shape[1] - self.padding_right_offset
|
||||
)
|
||||
+ new_padding_right_offset,
|
||||
]
|
||||
# Do the same for pixel_values and image_attention_mask
|
||||
pixel_values = self.pixel_values[keep_indices]
|
||||
self.image_attention_mask = self.image_attention_mask[
|
||||
keep_indices,
|
||||
-(self.padding_right_offset + max_input_length) : (
|
||||
self.image_attention_mask.shape[1] - self.padding_right_offset
|
||||
)
|
||||
+ new_padding_right_offset,
|
||||
:,
|
||||
]
|
||||
if self.image_hidden_states is None:
|
||||
image_hidden_states = None
|
||||
else:
|
||||
image_hidden_states = self.image_hidden_states[keep_indices]
|
||||
|
||||
# Ensure that past_key_values tensors can be updated in-place
|
||||
if type(self.past_key_values[0]) is tuple:
|
||||
self.past_key_values = [list(layer) for layer in self.past_key_values]
|
||||
|
||||
# Update tensors in-place to allow incremental garbage collection
|
||||
past_kv_length = max_input_length - 1
|
||||
for layer in self.past_key_values:
|
||||
past_keys, past_values = layer
|
||||
if len(past_keys.shape) == 3:
|
||||
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
|
||||
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
|
||||
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
|
||||
if self.keys_head_dim_last:
|
||||
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
|
||||
else:
|
||||
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
|
||||
del past_keys
|
||||
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
|
||||
del past_values
|
||||
|
||||
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.pixel_values = pixel_values
|
||||
self.image_hidden_states = image_hidden_states
|
||||
self.position_ids = position_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.max_input_length = max_input_length
|
||||
self.padding_right_offset = new_padding_right_offset
|
||||
self.max_tokens = max_tokens
|
||||
|
||||
return self
|
||||
|
||||
@classmethod
|
||||
@tracer.start_as_current_span("concatenate")
|
||||
def concatenate(
|
||||
cls, batches: List["IdeficsCausalLMBatch"]
|
||||
) -> "IdeficsCausalLMBatch":
|
||||
# It adds new requests to the batch
|
||||
# Used for padding
|
||||
total_batch_size = 0
|
||||
max_input_length = 0
|
||||
max_num_images = 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)
|
||||
max_num_images = max(max_num_images, batch.pixel_values.size(1))
|
||||
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 = []
|
||||
max_tokens = 0
|
||||
|
||||
# Batch tensors
|
||||
input_ids = None
|
||||
attention_mask = None
|
||||
position_ids = None
|
||||
pixel_values = None
|
||||
image_hidden_states = None
|
||||
image_attention_mask = None
|
||||
past_key_values = []
|
||||
|
||||
# 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)
|
||||
|
||||
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)
|
||||
|
||||
# We only concatenate batches that did at least one step
|
||||
if batch.past_key_values is None:
|
||||
raise ValueError("only concatenate prefilled batches")
|
||||
|
||||
# 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
|
||||
|
||||
# Create padded tensor
|
||||
if attention_mask is None:
|
||||
attention_mask = batch.attention_mask.new_zeros(
|
||||
(total_batch_size, max_input_length + padding_right_offset),
|
||||
)
|
||||
|
||||
curr_batch_max_num_images = batch.pixel_values.size(1)
|
||||
if pixel_values is None:
|
||||
pixel_values = batch.pixel_values.new_zeros(
|
||||
(total_batch_size, max_num_images, 3, 224, 224)
|
||||
)
|
||||
pixel_values[start_index:end_index, :curr_batch_max_num_images] = (
|
||||
batch.pixel_values
|
||||
)
|
||||
|
||||
if image_attention_mask is None:
|
||||
image_attention_mask = batch.image_attention_mask.new_zeros(
|
||||
(
|
||||
total_batch_size,
|
||||
max_input_length + padding_right_offset,
|
||||
max_num_images,
|
||||
)
|
||||
)
|
||||
|
||||
# We need to slice the attention mask to remove padding from previous steps
|
||||
# and to remove unused allocated space
|
||||
left_offset = max_input_length - batch.max_input_length
|
||||
batch_left_offset = (
|
||||
batch.attention_mask.shape[1]
|
||||
- batch.max_input_length
|
||||
- batch.padding_right_offset
|
||||
)
|
||||
attention_mask[
|
||||
start_index:end_index,
|
||||
left_offset:-padding_right_offset,
|
||||
] = batch.attention_mask[
|
||||
:,
|
||||
batch_left_offset : -batch.padding_right_offset,
|
||||
]
|
||||
image_attention_mask[
|
||||
start_index:end_index,
|
||||
left_offset:-padding_right_offset,
|
||||
:curr_batch_max_num_images,
|
||||
] = batch.image_attention_mask[
|
||||
:, batch_left_offset : -batch.padding_right_offset, :
|
||||
]
|
||||
|
||||
# Create empty tensor
|
||||
# position_ids is always of shape [batch_size, 1]
|
||||
if position_ids is None:
|
||||
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
|
||||
position_ids[start_index:end_index] = batch.position_ids
|
||||
|
||||
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
|
||||
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
|
||||
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
|
||||
# And ensure that we can update tensors in-place
|
||||
if isinstance(batch.past_key_values[0], tuple):
|
||||
batch.past_key_values = [
|
||||
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
|
||||
for layer in batch.past_key_values
|
||||
]
|
||||
elif len(batch.past_key_values[0][0].shape) == 3:
|
||||
for layer in batch.past_key_values:
|
||||
for k, t in enumerate(layer):
|
||||
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
|
||||
|
||||
# Add eventual padding tokens that were added while concatenating
|
||||
max_tokens += batch.max_tokens + (
|
||||
max_input_length - batch.max_input_length
|
||||
) * len(batch)
|
||||
|
||||
start_index = end_index
|
||||
|
||||
first_past_kvs = batches[0].past_key_values
|
||||
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
|
||||
|
||||
padded_past_values_shape = (
|
||||
total_batch_size,
|
||||
num_heads,
|
||||
max_input_length - 1,
|
||||
head_dim,
|
||||
)
|
||||
|
||||
if batches[0].keys_head_dim_last:
|
||||
padded_past_keys_shape = padded_past_values_shape
|
||||
else:
|
||||
# seq_length is last for BLOOM
|
||||
padded_past_keys_shape = (
|
||||
total_batch_size,
|
||||
num_heads,
|
||||
head_dim,
|
||||
max_input_length - 1,
|
||||
)
|
||||
|
||||
# Iterate over attention layers
|
||||
# Concatenate past key values layer by layer to allow incremental garbage collection
|
||||
for j in range(len(first_past_kvs)):
|
||||
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
|
||||
start_index = 0
|
||||
for batch in batches:
|
||||
past_keys = batch.past_key_values[j][0]
|
||||
# Clear reference to the original tensor
|
||||
batch.past_key_values[j][0] = None
|
||||
|
||||
# Slicing end index for this batch
|
||||
end_index = start_index + len(batch)
|
||||
# We slice the keys to remove the padding from previous batches
|
||||
past_seq_len = batch.max_input_length - 1
|
||||
if batch.keys_head_dim_last:
|
||||
padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
|
||||
past_keys[:, :, -past_seq_len:, :]
|
||||
)
|
||||
else:
|
||||
# BLOOM case
|
||||
padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
|
||||
past_keys[:, :, :, -past_seq_len:]
|
||||
)
|
||||
del past_keys
|
||||
|
||||
start_index = end_index
|
||||
|
||||
padded_past_values = first_past_kvs[j][1].new_zeros(
|
||||
padded_past_values_shape
|
||||
)
|
||||
start_index = 0
|
||||
for batch in batches:
|
||||
past_values = batch.past_key_values[j][1]
|
||||
# Clear reference to the original tensor
|
||||
batch.past_key_values[j][1] = None
|
||||
|
||||
# Slicing end index for this batch
|
||||
end_index = start_index + len(batch)
|
||||
# We slice the past values to remove the padding from previous batches
|
||||
past_seq_len = batch.max_input_length - 1
|
||||
padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
|
||||
past_values[:, :, -past_seq_len:, :]
|
||||
)
|
||||
del past_values
|
||||
|
||||
# Update values
|
||||
start_index = end_index
|
||||
|
||||
past_key_values.append([padded_past_keys, padded_past_values])
|
||||
|
||||
return cls(
|
||||
batch_id=batches[0].batch_id,
|
||||
requests=requests,
|
||||
requests_idx_mapping=requests_idx_mapping,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
pixel_values=pixel_values,
|
||||
image_hidden_states=image_hidden_states,
|
||||
image_attention_mask=image_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
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,
|
||||
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,
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.requests)
|
||||
|
||||
|
||||
class IdeficsCausalLM(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.quantize = quantize
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
device = torch.device("hpu")
|
||||
dtype = torch.bfloat16 if dtype is None else dtype
|
||||
self.device, self.dtype = device, dtype
|
||||
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.speculator = speculator
|
||||
config.vision_config.quantize = quantize
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
self.processor = AutoProcessor.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
weights_loader = get_loader(
|
||||
quantize=quantize, model_id=model_id, revision=revision
|
||||
)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
weights = Weights(
|
||||
filenames,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
process_group=self.process_group,
|
||||
weights_loader=weights_loader,
|
||||
)
|
||||
|
||||
model = IdeficsForVisionText2Text(config, weights)
|
||||
|
||||
self.config = config
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super().__init__(
|
||||
model_id=model_id,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
|
||||
@property
|
||||
def batch_type(self) -> Type[IdeficsCausalLMBatch]:
|
||||
return IdeficsCausalLMBatch
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
pixel_values,
|
||||
image_hidden_states,
|
||||
image_attention_mask,
|
||||
past_key_values: Optional = None,
|
||||
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
|
||||
# Model Forward
|
||||
kwargs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"pixel_values": pixel_values,
|
||||
"image_hidden_states": image_hidden_states,
|
||||
"image_attention_mask": image_attention_mask,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": True,
|
||||
"return_dict": True,
|
||||
}
|
||||
if self.has_position_ids:
|
||||
kwargs["position_ids"] = position_ids
|
||||
|
||||
outputs, speculative_logits = self.model.forward(**kwargs)
|
||||
return (
|
||||
outputs.logits,
|
||||
speculative_logits,
|
||||
outputs.past_key_values,
|
||||
outputs.image_hidden_states,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("generate_token")
|
||||
def generate_token(
|
||||
self, batch: IdeficsCausalLMBatch
|
||||
) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch], Tuple[int, int]]:
|
||||
start = time.time_ns()
|
||||
# slice the attention mask to the correct shape
|
||||
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
||||
if batch.image_attention_mask is None:
|
||||
image_attention_mask = None
|
||||
else:
|
||||
if batch.input_ids.size(1) == 1:
|
||||
# THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
|
||||
# but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
|
||||
# this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
|
||||
# token need to attend to the encoder hidden states (i.e. the vision encoder)
|
||||
# Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
|
||||
image_attention_mask = batch.image_attention_mask[
|
||||
:, -(batch.padding_right_offset + 1)
|
||||
].unsqueeze(1)
|
||||
else:
|
||||
image_attention_mask = batch.image_attention_mask[
|
||||
:, : -batch.padding_right_offset
|
||||
]
|
||||
|
||||
logits, speculative_logits, past, image_hidden_states = self.forward(
|
||||
input_ids=batch.input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=batch.position_ids,
|
||||
pixel_values=batch.pixel_values,
|
||||
image_hidden_states=batch.image_hidden_states,
|
||||
image_attention_mask=image_attention_mask,
|
||||
past_key_values=batch.past_key_values,
|
||||
)
|
||||
# Hardcoded remove image tokens
|
||||
logits[:, 32000:32001] = torch.finfo(logits.dtype).min
|
||||
|
||||
start_decode = time.time_ns()
|
||||
|
||||
# Results
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
|
||||
# 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,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
logits,
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
) 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
|
||||
|
||||
# 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")] + 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
|
||||
|
||||
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]
|
||||
|
||||
# Update attention_mask as we added a new token to input_ids
|
||||
batch.attention_mask[:, -batch.padding_right_offset] = 1
|
||||
batch.image_attention_mask[:, -batch.padding_right_offset, :] = (
|
||||
batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
|
||||
)
|
||||
# Decrease right offset
|
||||
batch.padding_right_offset -= 1
|
||||
|
||||
# Update position_ids
|
||||
batch.position_ids = batch.position_ids[:, -1:] + 1
|
||||
|
||||
# Update past key values
|
||||
batch.past_key_values = past
|
||||
batch.image_hidden_states = image_hidden_states
|
||||
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, batch, (forward_ns, decode_ns)
|
||||
@ -1,814 +0,0 @@
|
||||
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.quantize = quantize
|
||||
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)
|
||||
@ -1,47 +0,0 @@
|
||||
import torch
|
||||
from dataclasses import dataclass
|
||||
from typing import List, Optional, Type
|
||||
|
||||
from text_generation_server.models import CausalLM
|
||||
from text_generation_server.models.causal_lm import CausalLMBatch
|
||||
|
||||
|
||||
@dataclass
|
||||
class StarCoderCausalLMBatch(CausalLMBatch):
|
||||
past_key_values: Optional[List[torch.Tensor]]
|
||||
|
||||
def detach_kv_cache(self):
|
||||
past_keys = []
|
||||
past_values = []
|
||||
last_dim = int(self.past_key_values[0].size(dim=-1) / 2)
|
||||
for key_value in self.past_key_values:
|
||||
past_keys.append(key_value.split((last_dim, last_dim), dim=-1)[0])
|
||||
past_values.append(key_value.split((last_dim, last_dim), dim=-1)[1])
|
||||
del self.past_key_values
|
||||
|
||||
return past_keys, past_values
|
||||
|
||||
def attach_kv_cache(self, past_keys, past_values):
|
||||
self.past_key_values = [
|
||||
torch.cat((key, value), dim=-1)
|
||||
for key, value in zip(past_keys, past_values)
|
||||
]
|
||||
|
||||
|
||||
class StarCoder(CausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
):
|
||||
|
||||
super(StarCoder, self).__init__(
|
||||
model_id=model_id,
|
||||
revision=revision,
|
||||
dtype=dtype,
|
||||
)
|
||||
|
||||
@property
|
||||
def batch_type(self) -> Type[CausalLMBatch]:
|
||||
return StarCoderCausalLMBatch
|
||||
File diff suppressed because it is too large
Load Diff
@ -7,13 +7,5 @@ if [[ "$*" == *"--sharded true"* ]]; then
|
||||
echo 'setting PT_HPU_ENABLE_LAZY_COLLECTIVES=1 for sharding'
|
||||
export PT_HPU_ENABLE_LAZY_COLLECTIVES=1
|
||||
fi
|
||||
# Check if ATTENTION environment variable is set to paged
|
||||
if [[ "$ATTENTION" == "paged" ]]; then
|
||||
# Check if Llama-4 is in the command line arguments
|
||||
if [[ "$*" == *"Llama-4"* || "$*" == *"Qwen3"* ]]; then
|
||||
echo 'ATTENTION=paged and Llama-4 or Qwen3 detected'
|
||||
pip install git+https://github.com/huggingface/transformers.git@29338949
|
||||
fi
|
||||
fi
|
||||
|
||||
text-generation-launcher $@
|
||||
|
||||
Loading…
Reference in New Issue
Block a user