mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-10-09 15:05:24 +00:00
291 lines
11 KiB
Python
291 lines
11 KiB
Python
import torch
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import torch.distributed
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from accelerate import init_empty_weights
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from opentelemetry import trace
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from pathlib import Path
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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, Tuple, List
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_llama_modeling import (
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FlashLlamaForCausalLM,
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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 FlashLlama(FlashCausalLM):
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def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
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else:
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raise NotImplementedError("FlashCausalLM is only available on GPU")
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if quantize:
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raise NotImplementedError("FlashCausalLM does not support quantization")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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)
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config = AutoConfig.from_pretrained(
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model_id, revision=revision, tp_parallel=True
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)
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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 = FlashLlamaForCausalLM(config)
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self.load_weights(
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model,
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filenames,
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)
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self.model = model.eval().to(device).to(dtype)
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super(FlashCausalLM, self).__init__(
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tokenizer=tokenizer,
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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,
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filenames: List[Path],
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):
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final_state_dict = {}
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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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layer_name = ".".join(key.split(".")[:4])
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if "q_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][: value.shape[0]] = value
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elif "k_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][
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value.shape[0] : value.shape[0] * 2
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] = value
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elif "v_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][value.shape[0] * 2 :] = value
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elif "gate_proj" in key:
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final_key = layer_name + ".gate_up_proj.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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final_state_dict[final_key][: value.shape[0]] = value
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elif "up_proj" in key:
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final_key = layer_name + ".gate_up_proj.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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final_state_dict[final_key][value.shape[0] :] = value
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else:
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final_state_dict[key] = value
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del state_dict
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parameters = dict(model.named_parameters())
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for key, value in final_state_dict.items():
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current_parameter_tensor = parameters.get(key, None)
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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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if (
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current_parameter_tensor is not None
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and current_parameter_tensor.shape != value.shape
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):
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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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value = value.contiguous()
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if current_parameter_tensor is not None:
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module._parameters[param_name] = value
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else:
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module._buffers[param_name] = value
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model.post_load_weights()
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class FlashLlamaSharded(FlashLlama):
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def __init__(
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self, model_id: str, revision: Optional[str] = None, quantize: bool = False
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):
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self.process_group, self.rank, self.world_size = initialize_torch_distributed()
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self.master = self.rank == 0
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{self.rank}")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
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else:
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raise NotImplementedError("FlashLlama is only available on GPU")
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if quantize:
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raise NotImplementedError("FlashLlama does not support quantization")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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)
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config = AutoConfig.from_pretrained(
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model_id, revision=revision, tp_parallel=True
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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 = FlashGPTNeoXForCausalLM(config)
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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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rank=self.rank,
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world_size=self.world_size,
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)
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model.post_load_weights()
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self.model = model.eval().to(dtype)
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torch.distributed.barrier(group=self.process_group)
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super(FlashCausalLM, self).__init__(
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tokenizer=tokenizer,
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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,
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filenames: List[str],
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quantize: bool,
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device: torch.device,
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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 not quantize 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 == "embed_out.weight" and model.gpt_neox.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()
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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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def forward(
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self,
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input_ids: torch.Tensor,
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position_ids: torch.Tensor,
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cu_seqlens: torch.Tensor,
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max_s: int,
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past_key_values: Optional = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if self.model.gpt_neox.tp_embeddings:
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logits, present = self.model.forward(
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input_ids=input_ids,
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position_ids=position_ids,
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cu_seqlens=cu_seqlens,
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max_s=max_s,
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past_key_values=past_key_values,
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)
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# Logits are sharded, so we need to gather them
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world_logits = [torch.empty_like(logits) for _ in range(self.world_size)]
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torch.distributed.all_gather(world_logits, logits, group=self.process_group)
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world_logits = torch.cat(world_logits, dim=1)
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return world_logits, present
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# While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
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else:
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return super(FlashLlamaSharded, self).forward(
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input_ids, position_ids, cu_seqlens, max_s, past_key_values
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)
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