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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):
self.past_pad = None
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else 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__(
self, model_id: str, revision: Optional[str] = None, quantize: bool = False
):
self.past_pad = None
self.process_group, self.rank, self.world_size = initialize_torch_distributed()
self.master = self.rank == 0
if torch.cuda.is_available():
device = torch.device(f"cuda:{self.rank}")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else 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=self.rank,
world_size=self.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,
)
@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)
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