mirror of
https://github.com/huggingface/text-generation-inference.git
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76a48cd365
Changes only the type from `bool` to `Option<Enum>` pretty much everywhere. - Use `Optional[str]` in Python (easier to manage than importing type everywhere). Except for the cli to get proper validation - Updated all models to handle gracefully new values. (Error out if unknown value, or gptq since not implemented). <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
329 lines
12 KiB
Python
329 lines
12 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 AutoConfig
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from transformers.models.llama import LlamaTokenizer
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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_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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self.past_pad = None
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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("FlashLlama is only available on GPU")
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tokenizer = LlamaTokenizer.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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)
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config = AutoConfig.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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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(model, filenames, quantize, device, dtype)
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self.model = model.eval().to(device)
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super(FlashCausalLM, self).__init__(
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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,
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filenames: List[Path],
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quantize: bool,
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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 not quantize 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_proj" in key or "k_proj" in key or "v_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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# Fused gate and up projs
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elif "gate_proj" in key or "up_proj" in key:
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final_key = layer_name + ".gate_up_proj.weight"
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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 current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "query_key_value" in final_key:
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module._parameters[param_name] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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# Init gate and up proj
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elif "gate_up_proj" in final_key:
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module._parameters[param_name] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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# Copy to correct slice
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if "q_proj" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "k_proj" in key:
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module._parameters[param_name][
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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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module._parameters[param_name][value.shape[0] * 2 :] = value
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elif "gate_proj" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "up_proj" in key:
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module._parameters[param_name][value.shape[0] :] = 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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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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torch.cuda.empty_cache()
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model.post_load_weights(quantize)
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class FlashLlamaSharded(FlashLlama):
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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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):
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self.past_pad = None
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self.process_group, rank, world_size = initialize_torch_distributed()
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self.master = rank == 0
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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("FlashLlama is only available on GPU")
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tokenizer = LlamaTokenizer.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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)
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config = AutoConfig.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 = FlashLlamaForCausalLM(config, process_group=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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self.model = model.eval().to(device)
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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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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: bool,
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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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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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slice_ = f.get_slice(name)
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layer_name = ".".join(name.split(".")[:4])
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# Fused qkv
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if "q_proj" in name or "k_proj" in name or "v_proj" in name:
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final_name = layer_name + ".query_key_value.weight"
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# Fused gate and up projs
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elif "gate_proj" in name or "up_proj" in name:
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final_name = layer_name + ".gate_up_proj.weight"
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else:
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final_name = name
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module_name, param_name = final_name.rsplit(".", 1)
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module = model.get_submodule(module_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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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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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.model.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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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 current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "query_key_value" in final_name:
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module._parameters[param_name] = tensor.new_empty(
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(tensor.shape[0] * 3, tensor.shape[1])
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)
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# Init gate and up proj
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elif "gate_up_proj" in final_name:
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module._parameters[param_name] = tensor.new_empty(
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(tensor.shape[0] * 2, tensor.shape[1])
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)
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# Init gate and up proj
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if "q_proj" in name:
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module._parameters[param_name][: tensor.shape[0]] = tensor
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elif "k_proj" in name:
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module._parameters[param_name][
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tensor.shape[0] : tensor.shape[0] * 2
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] = tensor
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elif "v_proj" in name:
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module._parameters[param_name][
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tensor.shape[0] * 2 :
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] = tensor
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elif "gate_proj" in name:
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module._parameters[param_name][: tensor.shape[0]] = tensor
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elif "up_proj" in name:
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module._parameters[param_name][tensor.shape[0] :] = 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 {name} -- 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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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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torch.cuda.empty_cache()
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model.post_load_weights(quantize)
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