hf_text-generation-inference/server/text_generation_server/models/flash_llama.py

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import torch
import torch.distributed
from accelerate import init_empty_weights
from opentelemetry import trace
from pathlib import Path
from safetensors import safe_open
from transformers import AutoConfig
from transformers.models.llama import LlamaTokenizer
from typing import Optional, List
from text_generation_server.models import FlashCausalLM
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
FlashLlamaForCausalLM,
TensorParallelEmbedding,
TensorParallelRowLinear,
TensorParallelColumnLinear,
)
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
download_weights,
weight_hub_files,
LocalEntryNotFoundError,
)
tracer = trace.get_tracer(__name__)
class FlashLlama(FlashCausalLM):
def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16
else:
raise NotImplementedError("FlashLlama is only available on GPU")
tokenizer = LlamaTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
)
# We do not use from_pretrained as we modified the model internal module layout
try:
filenames = weight_files(model_id, revision, ".bin")
# Local files not found
except LocalEntryNotFoundError:
hub_files = weight_hub_files(model_id, revision, ".bin")
filenames = download_weights(hub_files, model_id, revision)
with init_empty_weights():
model = FlashLlamaForCausalLM(config)
self.load_weights(model, filenames, quantize, device, dtype)
self.model = model.eval().to(device)
super(FlashCausalLM, self).__init__(
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
)
@staticmethod
def load_weights(
model,
filenames: List[Path],
quantize: bool,
device: torch.device,
dtype: torch.dtype,
):
for filename in filenames:
state_dict = torch.load(filename, map_location="cpu")
for key, value in state_dict.items():
value = value.to(device if not quantize else "cpu").to(dtype)
layer_name = ".".join(key.split(".")[:4])
# Fused qkv
if "q_proj" in key or "k_proj" in key or "v_proj" in key:
final_key = layer_name + ".query_key_value.weight"
# Fused gate and up projs
elif "gate_proj" in key or "up_proj" in key:
final_key = layer_name + ".gate_up_proj.weight"
else:
final_key = key
module_name, param_name = final_key.rsplit(".", 1)
module = model.get_submodule(module_name)
try:
current_parameter_tensor = module._parameters[param_name]
except KeyError:
current_parameter_tensor = None
if current_parameter_tensor is not None:
if current_parameter_tensor.device == torch.device("meta"):
# Init qkv
if "query_key_value" in final_key:
module._parameters[param_name] = value.new_empty(
(value.shape[0] * 3, value.shape[1])
)
# Init gate and up proj
elif "gate_up_proj" in final_key:
module._parameters[param_name] = value.new_empty(
(value.shape[0] * 2, value.shape[1])
)
# Copy to correct slice
if "q_proj" in key:
module._parameters[param_name][: value.shape[0]] = value
elif "k_proj" in key:
module._parameters[param_name][
value.shape[0] : value.shape[0] * 2
] = value
elif "v_proj" in key:
module._parameters[param_name][value.shape[0] * 2 :] = value
elif "gate_proj" in key:
module._parameters[param_name][: value.shape[0]] = value
elif "up_proj" in key:
module._parameters[param_name][value.shape[0] :] = value
else:
if current_parameter_tensor.shape != value.shape:
raise ValueError(
f"Name {final_key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
)
module._parameters[param_name] = value
else:
module._buffers[param_name] = value
del value
torch.cuda.empty_cache()
model.post_load_weights(quantize)
class FlashLlamaSharded(FlashLlama):
def __init__(
feat(server): GPTQ quantization (step1) (#277) 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 -->
2023-05-12 06:46:41 -06:00
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
):
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")
dtype = torch.float16
else:
raise NotImplementedError("FlashLlama is only available on GPU")
tokenizer = LlamaTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
)
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
)
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
with init_empty_weights():
model = FlashLlamaForCausalLM(config, process_group=self.process_group)
torch.distributed.barrier(group=self.process_group)
self.load_weights(
model,
filenames,
quantize=quantize,
device=device,
dtype=dtype,
rank=rank,
world_size=world_size,
)
self.model = model.eval().to(device)
torch.distributed.barrier(group=self.process_group)
super(FlashCausalLM, self).__init__(
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
)
@staticmethod
def load_weights(
model,
filenames: List[str],
quantize: bool,
device: torch.device,
dtype: torch.dtype,
rank: int,
world_size: int,
):
for file in filenames:
with safe_open(
file, framework="pt", device=str(device) if not quantize else "cpu"
) as f:
for name in f.keys():
slice_ = f.get_slice(name)
layer_name = ".".join(name.split(".")[:4])
# Fused qkv
if "q_proj" in name or "k_proj" in name or "v_proj" in name:
final_name = layer_name + ".query_key_value.weight"
# Fused gate and up projs
elif "gate_proj" in name or "up_proj" in name:
final_name = layer_name + ".gate_up_proj.weight"
else:
final_name = name
module_name, param_name = final_name.rsplit(".", 1)
module = model.get_submodule(module_name)
if isinstance(module, TensorParallelColumnLinear):
size = slice_.get_shape()[0]
block_size = size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensor = slice_[start:stop]
elif isinstance(module, TensorParallelRowLinear):
size = slice_.get_shape()[1]
block_size = size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensor = slice_[:, start:stop]
elif isinstance(module, TensorParallelEmbedding):
size = slice_.get_shape()[0]
block_size = size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensor = slice_[start:stop]
elif name == "lm_head.weight" and model.model.tp_embeddings:
size = slice_.get_shape()[0]
block_size = size // world_size
start = rank * block_size
stop = (rank + 1) * block_size
tensor = slice_[start:stop]
else:
try:
tensor = slice_[:]
except:
tensor = f.get_tensor(name)
tensor = tensor.contiguous().to(dtype)
try:
current_parameter_tensor = module._parameters[param_name]
except KeyError:
current_parameter_tensor = None
if current_parameter_tensor is not None:
if current_parameter_tensor.device == torch.device("meta"):
# Init qkv
if "query_key_value" in final_name:
module._parameters[param_name] = tensor.new_empty(
(tensor.shape[0] * 3, tensor.shape[1])
)
# Init gate and up proj
elif "gate_up_proj" in final_name:
module._parameters[param_name] = tensor.new_empty(
(tensor.shape[0] * 2, tensor.shape[1])
)
# Init gate and up proj
if "q_proj" in name:
module._parameters[param_name][: tensor.shape[0]] = tensor
elif "k_proj" in name:
module._parameters[param_name][
tensor.shape[0] : tensor.shape[0] * 2
] = tensor
elif "v_proj" in name:
module._parameters[param_name][
tensor.shape[0] * 2 :
] = tensor
elif "gate_proj" in name:
module._parameters[param_name][: tensor.shape[0]] = tensor
elif "up_proj" in name:
module._parameters[param_name][tensor.shape[0] :] = tensor
else:
if current_parameter_tensor.shape != tensor.shape:
raise ValueError(
f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
)
module._parameters[param_name] = tensor
else:
module._buffers[param_name] = tensor
torch.cuda.empty_cache()
model.post_load_weights(quantize)