411 lines
16 KiB
Python
411 lines
16 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 safetensors import safe_open
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from pathlib import Path
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from transformers import AutoTokenizer, GPT2Config
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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_santacoder_modeling import (
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FlashSantacoderForCausalLM,
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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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 FlashSantacoder(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("FlashSantacoder 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 = GPT2Config.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 we modified the model internal module layout
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filenames = weight_files(model_id, revision, ".safetensors")
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with init_empty_weights():
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model = FlashSantacoderForCausalLM(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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config.architectures[0].startswith("GPT2"),
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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: FlashSantacoderForCausalLM,
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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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transpose: bool,
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):
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for filename in filenames:
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with safe_open(
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filename,
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framework="pt",
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device=str(device) if quantize is None else "cpu",
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) as f:
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for key in f.keys():
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value = f.get_tensor(key)
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value = value.to(device if quantize is None else "cpu").to(dtype)
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layer_name = ".".join(key.split(".")[:4])
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# Fused qkv
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if "q_attn.weight" in key or "kv_attn.weight" in key:
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final_key = layer_name + ".c_attn.weight"
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elif "q_attn.bias" in key or "kv_attn.bias" in key:
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final_key = layer_name + ".c_attn.bias"
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else:
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final_key = key
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module_name, param_name = final_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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except KeyError:
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current_parameter_tensor = None
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if current_parameter_tensor is not None:
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if transpose and (
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"c_fc.weight" in key
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or "c_proj.weight" in key
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or "q_attn.weight" in key
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or "kv_attn.weight" in key
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or "c_attn.weight" in key
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):
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# Tranpose as we use nn.Linear instead of Conv1D
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value = value.T
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if current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "c_attn.weight" in final_key:
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module._parameters[param_name] = value.new_empty(
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(
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model.transformer.head_size
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* (model.transformer.num_heads + 2),
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value.shape[1],
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)
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)
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elif "c_attn.bias" in final_key:
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module._parameters[param_name] = value.new_empty(
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(
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model.transformer.head_size
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* (model.transformer.num_heads + 2)
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)
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)
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# Copy to correct slice
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if "q_attn.weight" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "q_attn.bias" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "kv_attn.weight" in key:
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module._parameters[param_name][
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model.transformer.head_size
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* model.transformer.num_heads :
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] = value
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elif "kv_attn.bias" in key:
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module._parameters[param_name][
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model.transformer.head_size
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* model.transformer.num_heads :
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] = value
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else:
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if current_parameter_tensor.shape != value.shape:
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raise ValueError(
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f"Name {final_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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else:
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module._buffers[param_name] = value
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del value
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if model.lm_head.weight.device == torch.device("meta"):
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model.lm_head.weight = torch.nn.Parameter(model.transformer.wte.weight)
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torch.cuda.empty_cache()
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model.post_load_weights(quantize)
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uninitialized_parameters = []
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for n, p in model.named_parameters():
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if p.data.device == torch.device("meta"):
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uninitialized_parameters.append(n)
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if uninitialized_parameters:
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raise RuntimeError(
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f"found uninitialized parameters in model : {uninitialized_parameters}"
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)
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def decode(self, generated_ids: List[int]) -> str:
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# Do not skip special tokens as they are used for custom parsing rules of the generated text
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
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)
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class FlashSantacoderSharded(FlashSantacoder):
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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("FlashSantacoderSharded 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 = GPT2Config.from_pretrained(
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model_id,
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revision=revision,
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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 = FlashSantacoderForCausalLM(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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transpose=config.architectures[0].startswith("GPT2"),
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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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transpose: bool,
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):
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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 key in f.keys():
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slice_ = f.get_slice(key)
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layer_name = ".".join(key.split(".")[:4])
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# Fused qkv
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if "q_attn.weight" in key or "kv_attn.weight" in key:
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final_key = layer_name + ".c_attn.weight"
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elif "q_attn.bias" in key or "kv_attn.bias" in key:
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final_key = layer_name + ".c_attn.bias"
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else:
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final_key = key
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module_name, param_name = final_key.rsplit(".", 1)
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module = model.get_submodule(module_name)
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if isinstance(module, TensorParallelColumnLinear):
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dim = 1 if transpose and "weight" in param_name else 0
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size = slice_.get_shape()[dim]
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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 = (
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slice_[start:stop] if dim == 0 else slice_[:, start:stop]
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)
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elif isinstance(module, TensorParallelRowLinear):
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if param_name == "weight":
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dim = 0 if transpose else 1
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size = slice_.get_shape()[dim]
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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 = (
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slice_[start:stop]
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if dim == 0
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else slice_[:, start:stop]
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)
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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 key == "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(key)
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tensor = tensor.contiguous().to(dtype)
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try:
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current_parameter_tensor = module._parameters[param_name]
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except KeyError:
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current_parameter_tensor = None
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if current_parameter_tensor is not None:
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if transpose and (
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"c_fc.weight" in key
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or "c_proj.weight" in key
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or "q_attn.weight" in key
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or "kv_attn.weight" in key
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or "c_attn.weight" in key
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):
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# Tranpose as we use nn.Linear instead of Conv1D
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tensor = tensor.T
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if current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "c_attn.weight" in final_key:
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module._parameters[param_name] = tensor.new_empty(
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(
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model.transformer.head_size
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* (model.transformer.num_heads + 2),
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tensor.shape[1],
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)
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)
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elif "c_attn.bias" in final_key:
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module._parameters[param_name] = tensor.new_empty(
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(
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model.transformer.head_size
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* (model.transformer.num_heads + 2)
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)
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)
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# Copy to correct slice
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if "q_attn" in key:
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size = tensor.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 = tensor[start:stop]
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module._parameters[param_name][: tensor.shape[0]] = tensor
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elif "kv_attn.weight" in key:
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module._parameters[param_name][
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model.transformer.head_size
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* model.transformer.num_heads :
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] = tensor
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elif "kv_attn.bias" in key:
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module._parameters[param_name][
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model.transformer.head_size
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* model.transformer.num_heads :
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] = tensor
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elif "c_attn" in key:
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# Slice q_tensor by shard
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q_tensor = tensor[: -2 * model.transformer.head_size]
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block_size = q_tensor.shape[0] // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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q_tensor = q_tensor[start:stop]
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module._parameters[param_name][
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: q_tensor.shape[0]
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] = q_tensor
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# Kv tensor is copied for every shard
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kv_tensor = tensor[-2 * model.transformer.head_size :]
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module._parameters[param_name][
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q_tensor.shape[0] :
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] = kv_tensor
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else:
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if current_parameter_tensor.shape != tensor.shape:
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raise ValueError(
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f"Name {key} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
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)
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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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if model.lm_head.weight.device == torch.device("meta"):
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model.lm_head.weight = torch.nn.Parameter(model.transformer.wte.weight)
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torch.cuda.empty_cache()
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model.post_load_weights(quantize)
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