278 lines
9.9 KiB
Python
278 lines
9.9 KiB
Python
import torch
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import torch.distributed
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from typing import List, Optional, Type
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from accelerate import init_empty_weights
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from safetensors import safe_open
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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AutoConfig,
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PreTrainedTokenizerBase,
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)
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from transformers.models.bloom.parallel_layers import (
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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TensorParallelRowLinear,
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)
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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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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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)
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HAS_BITS_AND_BYTES = True
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try:
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import bitsandbytes as bnb
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from bitsandbytes.nn import Int8Params
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except Exception as e:
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HAS_BITS_AND_BYTES = False
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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(BloomCausalLMBatch, cls).from_pb(
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pb=pb, tokenizer=tokenizer, dtype=dtype, 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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quantize: Optional[str] = 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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quantize=quantize,
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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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class BLOOMSharded(BLOOM):
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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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quantize: Optional[str] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16
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else:
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device = torch.device("cpu")
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dtype = torch.float32
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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config = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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slow_but_exact=False,
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tp_parallel=True,
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trust_remote_code=trust_remote_code,
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)
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config.pad_token_id = 3
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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with init_empty_weights():
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model = AutoModelForCausalLM.from_config(
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config, trust_remote_code=trust_remote_code
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)
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torch.distributed.barrier(group=self.process_group)
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self.load_weights(
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model,
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filenames,
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quantize=quantize,
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device=device,
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dtype=dtype,
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rank=rank,
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world_size=world_size,
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)
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torch.distributed.barrier(group=self.process_group)
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super(CausalLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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dtype=dtype,
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device=device,
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rank=rank,
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world_size=world_size,
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)
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@staticmethod
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def load_weights(
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model,
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filenames: List[str],
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quantize: Optional[str],
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device: torch.device,
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dtype: torch.dtype,
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rank: int,
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world_size: int,
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):
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parameters = dict(model.named_parameters())
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for file in filenames:
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with safe_open(
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file, framework="pt", device=str(device) if quantize is None else "cpu"
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) as f:
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for name in f.keys():
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if name.startswith("transformer.") or name.startswith("lm_head."):
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full_name = name
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else:
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full_name = f"transformer.{name}"
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module_name, param_name = full_name.rsplit(".", 1)
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module = model.get_submodule(module_name)
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current_tensor = parameters[full_name]
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slice_ = f.get_slice(name)
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if isinstance(module, TensorParallelColumnLinear):
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size = slice_.get_shape()[0]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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elif isinstance(module, TensorParallelRowLinear):
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if param_name == "weight":
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size = slice_.get_shape()[1]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[:, start:stop]
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else:
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tensor = slice_[:]
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# XXX: Hack for Rowlinear to add the bias only once.
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if rank != 0:
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tensor = torch.zeros_like(tensor)
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elif (
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isinstance(module, TensorParallelEmbedding)
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or name == "lm_head.weight"
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):
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size = slice_.get_shape()[0]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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else:
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tensor = slice_[:]
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if current_tensor.shape != tensor.shape:
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raise ValueError(
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f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}"
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)
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tensor = tensor.contiguous().to(dtype)
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if quantize == "bitsandbytes":
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if not HAS_BITS_AND_BYTES:
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raise ImportError(
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"bitsandbytes is not available on your machine either because it is not installed "
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"or you don't have a GPU.\n"
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"You can install it with `pip install bitsandbytes`."
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)
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if (
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type(module)
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in [TensorParallelRowLinear, TensorParallelColumnLinear]
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and param_name == "weight"
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):
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tensor = Int8Params(
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tensor,
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has_fp16_weights=False,
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requires_grad=False,
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).to(device)
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state = bnb.MatmulLtState()
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state.threshold = 6.0
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state.has_fp16_weights = False
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state.memory_efficient_backward = False
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state.use_pool = True
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state.CB = tensor.CB
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state.SCB = tensor.SCB
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tensor.CB = None
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tensor.SCB = None
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def replace_linear(state):
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def linear(input, weight, bias):
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out = bnb.matmul(
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input,
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weight,
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state=state,
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threshold=state.threshold,
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bias=bias,
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)
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if state.CB is not None:
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# we converted 8-bit row major to turing/ampere format
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# in the first inference pass
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# we no longer need the row-major weight
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del state.CB
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weight.data = state.CxB
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return out
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return linear
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module.linear = replace_linear(state)
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else:
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tensor = tensor.to(device)
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elif quantize == "gptq":
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raise NotImplementedError("`gptq` is not implemented for now")
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elif quantize is None:
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tensor = tensor.to(device)
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else:
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raise ValueError(f"Unexpected quantize `{quantize}`")
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module._parameters[param_name] = tensor
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if name == "word_embeddings.weight":
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model.lm_head._parameters["weight"] = tensor
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def forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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):
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outputs = self.model.forward(
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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=past_key_values,
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use_cache=True,
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)
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# Logits are sharded, so we need to gather them
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logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
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torch.distributed.all_gather(logits, outputs.logits, group=self.process_group)
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logits = torch.cat(logits, dim=2)
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return logits, outputs.past_key_values
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