245 lines
8.3 KiB
Python
245 lines
8.3 KiB
Python
import torch
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import torch.distributed
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from pathlib import Path
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from accelerate import init_empty_weights
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from opentelemetry import trace
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from safetensors import safe_open
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from transformers import AutoTokenizer, AutoConfig
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from typing import Optional, List
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_rw_modeling import (
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RWConfig,
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FlashRWForCausalLM,
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TensorParallelEmbedding,
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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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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download_weights,
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weight_hub_files,
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LocalEntryNotFoundError,
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)
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tracer = trace.get_tracer(__name__)
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class FlashRW(FlashCausalLM):
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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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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.float16
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else:
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raise NotImplementedError("RW is only available on GPU")
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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 = RWConfig.from_pretrained(
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model_id,
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revision=revision,
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)
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# We do not use from_pretrained as it is too slow
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try:
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filenames = weight_files(model_id, revision, ".bin")
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# Local files not found
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except LocalEntryNotFoundError:
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hub_files = weight_hub_files(model_id, revision, ".bin")
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filenames = download_weights(hub_files, model_id, revision)
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with init_empty_weights():
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model = FlashRWForCausalLM(config)
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self.load_weights(
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model,
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filenames,
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quantize,
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device,
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dtype,
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)
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super(FlashCausalLM, self).__init__(
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model=model.to(device),
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tokenizer=tokenizer,
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requires_padding=False,
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dtype=dtype,
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device=device,
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)
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@staticmethod
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def load_weights(
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model: FlashRWForCausalLM,
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filenames: List[Path],
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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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):
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for filename in filenames:
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state_dict = torch.load(filename, map_location="cpu")
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for key, value in state_dict.items():
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value = value.to(device if quantize is None else "cpu").to(dtype)
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module_name, param_name = key.rsplit(".", 1)
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module = model.get_submodule(module_name)
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try:
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current_parameter_tensor = module._parameters[param_name]
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if current_parameter_tensor.shape != value.shape:
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raise ValueError(
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f"Name {key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
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)
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module._parameters[param_name] = value
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except KeyError:
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module._buffers[param_name] = value
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del value
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torch.cuda.empty_cache()
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model.post_load_weights(quantize)
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class FlashRWSharded(FlashRW):
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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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raise NotImplementedError("FlashRW is only available on GPU")
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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 = RWConfig.from_pretrained(
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model_id, revision=revision, 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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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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with init_empty_weights():
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model = FlashRWForCausalLM(config, self.process_group)
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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(FlashCausalLM, self).__init__(
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model=model.to(device),
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tokenizer=tokenizer,
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requires_padding=False,
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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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module_name, param_name = name.rsplit(".", 1)
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module = model.get_submodule(module_name)
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current_parameter_tensor = parameters.get(name, None)
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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 isinstance(module, TensorParallelEmbedding):
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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 name == "lm_head.weight" and model.transformer.tp_embeddings:
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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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try:
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tensor = slice_[:]
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except:
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tensor = f.get_tensor(name)
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if (
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current_parameter_tensor is not None
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and current_parameter_tensor.shape != tensor.shape
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):
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raise ValueError(
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f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
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)
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tensor = tensor.contiguous().to(dtype)
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if current_parameter_tensor is not None:
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module._parameters[param_name] = tensor
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else:
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module._buffers[param_name] = tensor
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model.post_load_weights(quantize)
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