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inference/vllm/vllm/transformers_utils/configs/yi.py 0 → 100644
View file @ 24eacbc0
""" Yi model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class YiConfig(PretrainedConfig):
r"""
Reference:
https://huggingface.co/01-ai/Yi-6B/blob/main/configuration_yi.py
"""
model_type = "Yi"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=64000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
output_attentions=False,
rope_theta=5000000.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.output_attentions = output_attentions
self.rope_theta = rope_theta
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
inference/vllm/vllm/transformers_utils/tokenizer.py 0 → 100644
View file @ 24eacbc0
from typing import List, Optional, Tuple, Union
from transformers import (AutoTokenizer, PreTrainedTokenizer,
PreTrainedTokenizerFast)
from vllm.logger import init_logger
logger = init_logger(__name__)
# A fast LLaMA tokenizer with the pre-processed `tokenizer.json` file.
_FAST_LLAMA_TOKENIZER = "hf-internal-testing/llama-tokenizer"
def get_tokenizer(
tokenizer_name: str,
*args,
tokenizer_mode: str = "auto",
trust_remote_code: bool = False,
tokenizer_revision: Optional[str] = None,
**kwargs,
) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
"""Gets a tokenizer for the given model name via Huggingface."""
# # TODO：
if tokenizer_mode == "slow":
if kwargs.get("use_fast", False):
raise ValueError(
"Cannot use the fast tokenizer in slow tokenizer mode.")
kwargs["use_fast"] = False
if ("llama" in tokenizer_name.lower() and kwargs.get("use_fast", True)
and tokenizer_name != _FAST_LLAMA_TOKENIZER):
logger.info(
"For some LLaMA V1 models, initializing the fast tokenizer may "
"take a long time. To reduce the initialization time, consider "
f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
"tokenizer.")
try:
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name,
*args,
trust_remote_code=trust_remote_code,
tokenizer_revision=tokenizer_revision,
**kwargs)
except TypeError as e:
# The LLaMA tokenizer causes a protobuf error in some environments.
err_msg = (
"Failed to load the tokenizer. If you are using a LLaMA V1 model "
f"consider using '{_FAST_LLAMA_TOKENIZER}' instead of the "
"original tokenizer.")
raise RuntimeError(err_msg) from e
except ValueError as e:
# If the error pertains to the tokenizer class not existing or not
# currently being imported, suggest using the --trust-remote-code flag.
if (not trust_remote_code and
("does not exist or is not currently imported." in str(e)
or "requires you to execute the tokenizer file" in str(e))):
err_msg = (
"Failed to load the tokenizer. If the tokenizer is a custom "
"tokenizer not yet available in the HuggingFace transformers "
"library, consider setting `trust_remote_code=True` in LLM "
"or using the `--trust-remote-code` flag in the CLI.")
raise RuntimeError(err_msg) from e
else:
raise e
if not isinstance(tokenizer, PreTrainedTokenizerFast):
logger.warning(
"Using a slow tokenizer. This might cause a significant "
"slowdown. Consider using a fast tokenizer instead.")
# from IPython import embed; embed(header="In tokenizatiion")
return tokenizer
def _convert_tokens_to_string_with_added_encoders(
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
output_tokens: List[str],
skip_special_tokens: bool,
spaces_between_special_tokens: bool,
) -> str:
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921
# NOTE(woosuk): The following code is slow because it runs a for loop over
# the output_tokens. In Python, running a for loop over a list can be slow
# even when the loop body is very simple.
sub_texts = []
current_sub_text = []
all_special_tokens = set(tokenizer.all_special_tokens)
for token in output_tokens:
if skip_special_tokens and token in all_special_tokens:
continue
if token in tokenizer.get_added_vocab():
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
sub_texts.append(sub_text)
if spaces_between_special_tokens:
return " ".join(sub_texts)
else:
return "".join(sub_texts)
# Based on
# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15
# under Apache 2.0 license
def detokenize_incrementally(
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
all_input_ids: List[int],
prev_tokens: Optional[List[str]],
prefix_offset: int = 0,
read_offset: int = 0,
skip_special_tokens: bool = False,
spaces_between_special_tokens: bool = True,
) -> Tuple[List[str], str, int, int]:
new_token_id = all_input_ids[-1]
# This is the first iteration for this sequence
if prev_tokens is None:
new_tokens = tokenizer.convert_ids_to_tokens(
all_input_ids, skip_special_tokens=skip_special_tokens)
output_tokens = new_tokens
# 5 is an arbitrary value that should work for all
# tokenizers (bigger = more conservative).
# Subtract 1 extra to account for the generated token.
prefix_offset = max(len(output_tokens) - 6, 0)
# If the first new token is a special token, we can't skip 1 extra token
if skip_special_tokens and new_token_id in tokenizer.all_special_ids:
read_offset = max(len(output_tokens), 0)
else:
read_offset = max(len(output_tokens) - 1, 0)
else:
# Put new_token_id in a list so skip_special_tokens is respected
new_tokens = tokenizer.convert_ids_to_tokens(
[new_token_id], skip_special_tokens=skip_special_tokens)
output_tokens = prev_tokens + new_tokens
# The prefix text is necessary only to defeat cleanup algorithms in
# the decode which decide to add a space or not depending on the
# surrounding ids.
if tokenizer.is_fast or not tokenizer.get_added_vocab():
prefix_text = tokenizer.convert_tokens_to_string(
output_tokens[prefix_offset:read_offset])
new_text = tokenizer.convert_tokens_to_string(
output_tokens[prefix_offset:])
else:
prefix_text = _convert_tokens_to_string_with_added_encoders(
tokenizer,
output_tokens[prefix_offset:read_offset],
skip_special_tokens=skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
)
new_text = _convert_tokens_to_string_with_added_encoders(
tokenizer,
output_tokens[prefix_offset:],
skip_special_tokens=skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
)
if len(new_text) > len(prefix_text) and not new_text.endswith("�"):
# utf-8 char at the end means it's a potential unfinished byte sequence
# from byte fallback tokenization.
# If it's in the middle, it's probably a real invalid id generated
# by the model
new_text = new_text[len(prefix_text):]
return new_tokens, new_text, read_offset, len(output_tokens)
else:
return new_tokens, "", prefix_offset, read_offset
inference/vllm/vllm/utils.py 0 → 100644
View file @ 24eacbc0
import enum
import uuid
from platform import uname
import psutil
import torch
from vllm import cuda_utils
class Device(enum.Enum):
GPU = enum.auto()
CPU = enum.auto()
class Counter:
def __init__(self, start: int = 0) -> None:
self.counter = start
def __next__(self) -> int:
i = self.counter
self.counter += 1
return i
def reset(self) -> None:
self.counter = 0
def get_max_shared_memory_bytes(gpu: int = 0) -> int:
"""Returns the maximum shared memory per thread block in bytes."""
# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html
cudaDevAttrMaxSharedMemoryPerBlockOptin = 97 # pylint: disable=invalid-name
max_shared_mem = cuda_utils.get_device_attribute(
cudaDevAttrMaxSharedMemoryPerBlockOptin, gpu)
return int(max_shared_mem)
def get_gpu_memory(gpu: int = 0) -> int:
"""Returns the total memory of the GPU in bytes."""
return torch.cuda.get_device_properties(gpu).total_memory
def get_cpu_memory() -> int:
"""Returns the total CPU memory of the node in bytes."""
return psutil.virtual_memory().total
def random_uuid() -> str:
return str(uuid.uuid4().hex)
def in_wsl() -> bool:
# Reference: https://github.com/microsoft/WSL/issues/4071
return "microsoft" in " ".join(uname()).lower()
inference/vllm/vllm/worker/cache_engine.py 0 → 100644
View file @ 24eacbc0
"""CacheEngine class for managing the KV cache."""
from typing import Dict, List, Tuple
import torch
from vllm import cache_ops
from vllm.config import CacheConfig, ModelConfig, ParallelConfig
from vllm.logger import init_logger
from vllm.utils import in_wsl
logger = init_logger(__name__)
KVCache = Tuple[torch.Tensor, torch.Tensor]
class CacheEngine:
"""Manages the KV cache.
This class is responsible for initializing and managing the GPU and CPU KV
caches. It also provides methods for performing KV cache operations, such
as swapping and copying.
"""
def __init__(
self,
cache_config: CacheConfig,
model_config: ModelConfig,
parallel_config: ParallelConfig,
) -> None:
self.cache_config = cache_config
self.model_config = model_config
self.parallel_config = parallel_config
self.head_size = model_config.get_head_size()
self.num_layers = model_config.get_num_layers(parallel_config)
self.num_heads = model_config.get_num_kv_heads(parallel_config)
self.dtype = model_config.dtype
self.block_size = cache_config.block_size
self.num_gpu_blocks = cache_config.num_gpu_blocks
self.num_cpu_blocks = cache_config.num_cpu_blocks
# Initialize the cache.
self.gpu_cache = self.allocate_gpu_cache()
self.cpu_cache = self.allocate_cpu_cache()
# Initialize the stream for caching operations.
self.cache_stream = torch.cuda.Stream()
assert self.cache_stream != torch.cuda.current_stream()
# Initialize the events for stream synchronization.
self.events = [torch.cuda.Event() for _ in range(self.num_layers)]
def get_key_block_shape(self) -> Tuple[int, int, int, int]:
element_size = torch.tensor([], dtype=self.dtype).element_size()
x = 16 // element_size
return (
self.num_heads,
self.head_size // x,
self.block_size,
x,
)
def get_value_block_shape(self) -> Tuple[int, int, int]:
return (
self.num_heads,
self.head_size,
self.block_size,
)
def allocate_gpu_cache(self) -> List[KVCache]:
gpu_cache: List[KVCache] = []
key_block_shape = self.get_key_block_shape()
value_block_shape = self.get_value_block_shape()
for _ in range(self.num_layers):
key_blocks = torch.empty(
size=(self.num_gpu_blocks, *key_block_shape),
dtype=self.dtype,
device="cuda",
)
value_blocks = torch.empty(
size=(self.num_gpu_blocks, *value_block_shape),
dtype=self.dtype,
device="cuda",
)
gpu_cache.append((key_blocks, value_blocks))
return gpu_cache
def allocate_cpu_cache(self) -> List[KVCache]:
cpu_cache: List[KVCache] = []
key_block_shape = self.get_key_block_shape()
value_block_shape = self.get_value_block_shape()
pin_memory = not in_wsl()
if not pin_memory:
# Pinning memory in WSL is not supported.
# https://docs.nvidia.com/cuda/wsl-user-guide/index.html#known-limitations-for-linux-cuda-applications
logger.warning("Using 'pin_memory=False' as WSL is detected. "
"This may slow down the performance.")
for _ in range(self.num_layers):
key_blocks = torch.empty(
size=(self.num_cpu_blocks, *key_block_shape),
dtype=self.dtype,
pin_memory=pin_memory,
)
value_blocks = torch.empty(
size=(self.num_cpu_blocks, *value_block_shape),
dtype=self.dtype,
pin_memory=pin_memory,
)
cpu_cache.append((key_blocks, value_blocks))
return cpu_cache
def _swap(
self,
src: List[KVCache],
dst: List[KVCache],
src_to_dst: Dict[int, int],
) -> None:
with torch.cuda.stream(self.cache_stream):
for i in range(self.num_layers):
src_key_cache, src_value_cache = src[i]
dst_key_cache, dst_value_cache = dst[i]
# Copy the key blocks.
cache_ops.swap_blocks(src_key_cache, dst_key_cache, src_to_dst)
# Copy the value blocks.
cache_ops.swap_blocks(src_value_cache, dst_value_cache,
src_to_dst)
event = self.events[i]
event.record(stream=self.cache_stream)
def swap_in(self, src_to_dst: Dict[int, int]) -> None:
self._swap(self.cpu_cache, self.gpu_cache, src_to_dst)
def swap_out(self, src_to_dst: Dict[int, int]) -> None:
self._swap(self.gpu_cache, self.cpu_cache, src_to_dst)
def copy(self, src_to_dsts: Dict[int, List[int]]) -> None:
key_caches = [key_cache for key_cache, _ in self.gpu_cache]
value_caches = [value_cache for _, value_cache in self.gpu_cache]
# NOTE(woosuk): This operation implicitly synchronizes the CPU and GPU.
cache_ops.copy_blocks(key_caches, value_caches, src_to_dsts)
@staticmethod
def get_cache_block_size(
block_size: int,
model_config: ModelConfig,
parallel_config: ParallelConfig,
) -> int:
head_size = model_config.get_head_size()
num_heads = model_config.get_num_kv_heads(parallel_config)
num_layers = model_config.get_num_layers(parallel_config)
key_cache_block = block_size * num_heads * head_size
value_cache_block = key_cache_block
total = num_layers * (key_cache_block + value_cache_block)
dtype_size = _get_dtype_size(model_config.dtype)
return dtype_size * total
def _get_dtype_size(dtype: torch.dtype) -> int:
return torch.tensor([], dtype=dtype).element_size()
inference/vllm/vllm/worker/worker.py 0 → 100644
View file @ 24eacbc0
"""A GPU worker class."""
import os
from typing import Dict, List, Tuple, Optional
import torch
import torch.distributed
from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
SchedulerConfig)
from vllm.model_executor import get_model, InputMetadata, set_random_seed
from vllm.model_executor.parallel_utils.parallel_state import (
initialize_model_parallel)
from vllm.sampling_params import SamplingParams, SamplingType
from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
from vllm.worker.cache_engine import CacheEngine
from vllm.utils import get_gpu_memory
class Worker:
"""A worker class that executes (a partition of) the model on a GPU.
Each worker is associated with a single GPU. The worker is responsible for
maintaining the KV cache and executing the model on the GPU. In case of
distributed inference, each worker is assigned a partition of the model.
"""
def __init__(
self,
model_config: ModelConfig,
parallel_config: ParallelConfig,
scheduler_config: SchedulerConfig,
rank: Optional[int] = None,
distributed_init_method: Optional[str] = None,
) -> None:
self.model_config = model_config
self.parallel_config = parallel_config
self.scheduler_config = scheduler_config
self.rank = rank
self.distributed_init_method = distributed_init_method
# Uninitialized cache engine. Will be initialized by
# self.init_cache_engine().
self.cache_config = None
self.block_size = None
self.sliding_window = None
self.cache_engine = None
self.cache_events = None
self.gpu_cache = None
def init_model(self):
# This env var set by Ray causes exceptions with graph building.
os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
# Env vars will be set by Ray.
self.rank = self.rank if self.rank is not None else int(
os.getenv("RANK", "-1"))
local_rank = int(os.getenv("LOCAL_RANK", "0"))
self.device = torch.device(f"cuda:{local_rank}")
if self.rank < 0:
raise ValueError("Invalid or unspecified rank.")
torch.cuda.set_device(self.device)
_check_if_gpu_supports_dtype(self.model_config.dtype)
# Initialize the distributed environment.
_init_distributed_environment(self.parallel_config, self.rank,
self.distributed_init_method)
# Initialize the model.
set_random_seed(self.model_config.seed)
self.model = get_model(self.model_config)
@torch.inference_mode()
def profile_num_available_blocks(
self,
block_size: int,
gpu_memory_utilization: float,
cpu_swap_space: int,
) -> Tuple[int, int]:
# Profile the memory usage of the model and get the maximum number of
# cache blocks that can be allocated with the remaining free memory.
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
# Profile memory usage with max_num_sequences sequences and the total
# number of tokens equal to max_num_batched_tokens.
# Enable top-k sampling to reflect the accurate memory usage.
vocab_size = self.model.config.vocab_size
sampling_params = SamplingParams(top_p=0.99, top_k=vocab_size - 1)
max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
max_num_seqs = self.scheduler_config.max_num_seqs
seqs = []
for group_id in range(max_num_seqs):
seq_len = (max_num_batched_tokens // max_num_seqs +
(group_id < max_num_batched_tokens % max_num_seqs))
seq_data = SequenceData([0] * seq_len)
seq = SequenceGroupMetadata(
request_id=str(group_id),
is_prompt=True,
seq_data={group_id: seq_data},
sampling_params=sampling_params,
block_tables=None,
)
seqs.append(seq)
input_tokens, input_positions, input_metadata = self._prepare_inputs(
seqs)
# Execute the model.
num_layers = self.model_config.get_num_layers(self.parallel_config)
self.model(
input_ids=input_tokens,
positions=input_positions,
kv_caches=[(None, None)] * num_layers,
input_metadata=input_metadata,
cache_events=None,
)
# Calculate the number of blocks that can be allocated with the
# profiled peak memory.
torch.cuda.synchronize()
peak_memory = torch.cuda.max_memory_allocated()
total_gpu_memory = get_gpu_memory()
cache_block_size = CacheEngine.get_cache_block_size(
block_size, self.model_config, self.parallel_config)
num_gpu_blocks = int(
(total_gpu_memory * gpu_memory_utilization - peak_memory) //
cache_block_size)
num_cpu_blocks = int(cpu_swap_space // cache_block_size)
num_gpu_blocks = max(num_gpu_blocks, 0)
num_cpu_blocks = max(num_cpu_blocks, 0)
torch.cuda.empty_cache()
# Reset the seed to ensure that the random state is not affected by
# the model initialization and profiling.
set_random_seed(self.model_config.seed)
return num_gpu_blocks, num_cpu_blocks
def init_cache_engine(self, cache_config: CacheConfig) -> None:
self.cache_config = cache_config
self.block_size = cache_config.block_size
self.sliding_window = cache_config.sliding_window
self.cache_engine = CacheEngine(self.cache_config, self.model_config,
self.parallel_config)
self.cache_events = self.cache_engine.events
self.gpu_cache = self.cache_engine.gpu_cache
def _prepare_inputs(
self,
seq_group_metadata_list: List[SequenceGroupMetadata],
) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]:
seq_groups: List[Tuple[List[int], SamplingParams]] = []
input_tokens: List[List[int]] = []
input_positions: List[List[int]] = []
slot_mapping: List[List[int]] = []
selected_token_indices: List[int] = []
selected_token_start_idx = 0
categorized_sample_indices = {t: [] for t in SamplingType}
categorized_sample_indices_start_idx = 0
# Add prompt tokens.
prompt_lens: List[int] = []
for seq_group_metadata in seq_group_metadata_list:
if not seq_group_metadata.is_prompt:
continue
seq_ids = list(seq_group_metadata.seq_data.keys())
sampling_params = seq_group_metadata.sampling_params
seq_groups.append((seq_ids, sampling_params))
# Use any sequence in the group.
seq_id = seq_ids[0]
seq_data = seq_group_metadata.seq_data[seq_id]
prompt_tokens = seq_data.get_token_ids()
prompt_len = len(prompt_tokens)
prompt_lens.append(prompt_len)
if sampling_params.prompt_logprobs is not None:
# NOTE: prompt token positions do not need sample, skip
categorized_sample_indices_start_idx += prompt_len - 1
categorized_sample_indices[sampling_params.sampling_type].append(
categorized_sample_indices_start_idx)
categorized_sample_indices_start_idx += 1
input_tokens.append(prompt_tokens)
# NOTE(woosuk): Here we assume that the first token in the prompt
# is always the first token in the sequence.
input_positions.append(list(range(prompt_len)))
if seq_group_metadata.block_tables is None:
# During memory profiling, the block tables are not initialized
# yet. In this case, we just use a dummy slot mapping.
slot_mapping.append([0] * prompt_len)
continue
# Compute the slot mapping.
slot_mapping.append([])
block_table = seq_group_metadata.block_tables[seq_id]
for i in range(prompt_len):
block_number = block_table[i // self.block_size]
block_offset = i % self.block_size
slot = block_number * self.block_size + block_offset
slot_mapping[-1].append(slot)
# Add generation tokens.
max_context_len = 0
max_num_blocks_per_seq = 0
context_lens: List[int] = []
generation_block_tables: List[List[int]] = []
max_seq_len = max(prompt_lens) if prompt_lens else 1
for i, seq_group_metadata in enumerate(seq_group_metadata_list):
if seq_group_metadata.is_prompt:
# We need to do this in this loop as we need to know max_seq_len
assert len(
seq_ids) == 1, "Prompt input should have only one seq."
sampling_params = seq_group_metadata.sampling_params
assert len(prompt_lens) == len(seq_group_metadata_list)
prompt_len = prompt_lens[i]
if sampling_params.prompt_logprobs is not None:
selected_token_indices.extend(
range(selected_token_start_idx,
selected_token_start_idx + prompt_len - 1))
selected_token_indices.append(selected_token_start_idx +
prompt_len - 1)
selected_token_start_idx += max_seq_len
continue
seq_ids = list(seq_group_metadata.seq_data.keys())
sampling_params = seq_group_metadata.sampling_params
seq_groups.append((seq_ids, sampling_params))
num_seqs = len(seq_ids)
selected_token_indices.extend(
range(selected_token_start_idx,
selected_token_start_idx + num_seqs))
selected_token_start_idx += num_seqs
categorized_sample_indices[sampling_params.sampling_type].extend(
range(categorized_sample_indices_start_idx,
categorized_sample_indices_start_idx + num_seqs))
categorized_sample_indices_start_idx += num_seqs
for seq_id in seq_ids:
seq_data = seq_group_metadata.seq_data[seq_id]
generation_token = seq_data.get_last_token_id()
input_tokens.append([generation_token])
context_len = seq_data.get_len()
position = context_len - 1
if self.sliding_window is not None:
context_len = min(context_len, self.sliding_window)
input_positions.append([position])
block_table = seq_group_metadata.block_tables[seq_id]
max_context_len = max(max_context_len, context_len)
max_num_blocks_per_seq = max(max_num_blocks_per_seq,
len(block_table))
context_lens.append(context_len)
block_number = block_table[position // self.block_size]
block_offset = position % self.block_size
slot = block_number * self.block_size + block_offset
slot_mapping.append([slot])
if self.sliding_window is not None:
sliding_window_blocks = (self.sliding_window //
self.block_size)
block_table = block_table[-sliding_window_blocks:]
generation_block_tables.append(block_table)
padded_input_tokens = [
_pad_to_max(tokens, max_seq_len, pad=0) for tokens in input_tokens
]
padded_input_positions = [
_pad_to_max(positions, max_seq_len, pad=0)
for positions in input_positions
]
padded_slot_mapping = [
_pad_to_max(mapping, max_seq_len, pad=-1)
for mapping in slot_mapping
]
padded_block_tables = [
_pad_to_max(block_table, max_num_blocks_per_seq, pad=0)
for block_table in generation_block_tables
]
# Convert to tensors.
tokens_tensor = torch.tensor(padded_input_tokens,
dtype=torch.long,
device="cuda")
positions_tensor = torch.tensor(padded_input_positions,
dtype=torch.long,
device="cuda")
slot_mapping_tensor = torch.tensor(padded_slot_mapping,
dtype=torch.long,
device="cuda")
context_lens_tensor = torch.tensor(context_lens,
dtype=torch.int,
device="cuda")
selected_token_indices = torch.tensor(selected_token_indices,
dtype=torch.long,
device="cuda")
categorized_sample_indices = {
t: torch.tensor(seq_ids, dtype=torch.int, device="cuda")
for t, seq_ids in categorized_sample_indices.items()
}
block_tables_tensor = torch.tensor(padded_block_tables,
dtype=torch.int,
device="cuda")
seq_data: Dict[int, SequenceData] = {}
for seq_group_metadata in seq_group_metadata_list:
seq_data.update(seq_group_metadata.seq_data)
input_metadata = InputMetadata(
seq_groups=seq_groups,
seq_data=seq_data,
prompt_lens=prompt_lens,
slot_mapping=slot_mapping_tensor,
context_lens=context_lens_tensor,
max_context_len=max_context_len,
block_tables=block_tables_tensor,
selected_token_indices=selected_token_indices,
categorized_sample_indices=categorized_sample_indices,
sliding_window=self.sliding_window,
)
return tokens_tensor, positions_tensor, input_metadata
@torch.inference_mode()
def execute_model(
self,
seq_group_metadata_list: List[SequenceGroupMetadata],
blocks_to_swap_in: Dict[int, int],
blocks_to_swap_out: Dict[int, int],
blocks_to_copy: Dict[int, List[int]],
) -> SamplerOutput:
# Issue cache operations.
issued_cache_op = False
if blocks_to_swap_in:
self.cache_engine.swap_in(blocks_to_swap_in)
issued_cache_op = True
if blocks_to_swap_out:
self.cache_engine.swap_out(blocks_to_swap_out)
issued_cache_op = True
if blocks_to_copy:
self.cache_engine.copy(blocks_to_copy)
issued_cache_op = True
if issued_cache_op:
cache_events = self.cache_events
else:
cache_events = None
# If there is no input, we don't need to execute the model.
if not seq_group_metadata_list:
if cache_events is not None:
for event in cache_events:
event.wait()
return {}
# Prepare input tensors.
input_tokens, input_positions, input_metadata = self._prepare_inputs(
seq_group_metadata_list)
# Execute the model.
output = self.model(
input_ids=input_tokens,
positions=input_positions,
kv_caches=self.gpu_cache,
input_metadata=input_metadata,
cache_events=cache_events,
)
return output
def _init_distributed_environment(
parallel_config: ParallelConfig,
rank: int,
distributed_init_method: Optional[str] = None,
) -> None:
"""Initialize the distributed environment."""
if torch.distributed.is_initialized():
torch_world_size = torch.distributed.get_world_size()
if torch_world_size != parallel_config.world_size:
raise RuntimeError(
"torch.distributed is already initialized but the torch world "
"size does not match parallel_config.world_size "
f"({torch_world_size} vs. {parallel_config.world_size}).")
elif not distributed_init_method:
raise ValueError(
"distributed_init_method must be set if torch.distributed "
"is not already initialized")
else:
torch.distributed.init_process_group(
backend="nccl",
world_size=parallel_config.world_size,
rank=rank,
init_method=distributed_init_method,
)
# A small all_reduce for warmup.
torch.distributed.all_reduce(torch.zeros(1).cuda())
initialize_model_parallel(parallel_config.tensor_parallel_size,
parallel_config.pipeline_parallel_size)
def _pad_to_alignment(x: List[int], multiple_of: int, pad: int) -> List[int]:
return x + [pad] * ((-len(x)) % multiple_of)
def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
return x + [pad] * (max_len - len(x))
def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
# Check if the GPU supports the dtype.
if torch_dtype == torch.bfloat16:
compute_capability = torch.cuda.get_device_capability()
if compute_capability[0] < 8:
gpu_name = torch.cuda.get_device_name()
raise ValueError(
"Bfloat16 is only supported on GPUs with compute capability "
f"of at least 8.0. Your {gpu_name} GPU has compute capability "
f"{compute_capability[0]}.{compute_capability[1]}.")
log.txt 0 → 100644
View file @ 24eacbc0
s': 4.996971607208252, 'eval_runtime': 1009.8753, 'eval_samples_per_second': 1.06, 'eval_steps_per_second': 1.06, 'epoch': 0.01}
50%|████████████████████████████████████████████████████████████████████████████████████████████████ | 1500/3000 [1:34:10<45:25, 1.82s/itSaving model checkpoint to output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500
/usr/local/lib/python3.8/site-packages/peft/utils/save_and_load.py:148: UserWarning: Could not find a config file in checkpoint/miniCPM-bf16/ - will assume that the vocabulary was not modified.
warnings.warn(
tokenizer config file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/tokenizer_config.json
Special tokens file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/special_tokens_map.json
[2024-02-20 16:35:20,179] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step1500 is about to be saved!
/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[2024-02-20 16:35:20,222] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_model_states.pt
[2024-02-20 16:35:20,222] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_model_states.pt...
[2024-02-20 16:35:20,260] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_model_states.pt.
[2024-02-20 16:35:20,261] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_optim_states.pt...
[2024-02-20 16:35:20,378] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_optim_states.pt.
[2024-02-20 16:35:20,386] [INFO] [engine.py:3417:_save_zero_checkpoint] zero checkpoint saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-1500/global_step1500/zero_pp_rank_0_mp_rank_00_optim_states.pt
[2024-02-20 16:35:20,397] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step1500 is ready now!
{'loss': 5.3635, 'learning_rate': 0.0005144827586206897, 'epoch': 0.01}
{'loss': 5.1967, 'learning_rate': 0.0005110344827586208, 'epoch': 0.01}
{'loss': 5.0065, 'learning_rate': 0.0005075862068965517, 'epoch': 0.01}
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{'loss': 4.9256, 'learning_rate': 0.00048689655172413796, 'epoch': 0.01}
{'loss': 4.9216, 'learning_rate': 0.00048344827586206896, 'epoch': 0.01}
{'loss': 5.1224, 'learning_rate': 0.00048, 'epoch': 0.01}
{'loss': 4.6957, 'learning_rate': 0.000476551724137931, 'epoch': 0.01}
{'loss': 5.1196, 'learning_rate': 0.0004731034482758621, 'epoch': 0.01}
{'loss': 4.7971, 'learning_rate': 0.0004696551724137931, 'epoch': 0.01}
{'loss': 4.9963, 'learning_rate': 0.0004662068965517242, 'epoch': 0.01}
{'loss': 4.8869, 'learning_rate': 0.0004627586206896552, 'epoch': 0.01}
{'loss': 5.0425, 'learning_rate': 0.00045931034482758624, 'epoch': 0.01}
{'loss': 4.9879, 'learning_rate': 0.00045586206896551724, 'epoch': 0.01}
{'loss': 4.7501, 'learning_rate': 0.0004524137931034483, 'epoch': 0.01}
{'loss': 5.0549, 'learning_rate': 0.0004489655172413793, 'epoch': 0.01}
{'loss': 4.8451, 'learning_rate': 0.0004455172413793104, 'epoch': 0.01}
{'loss': 4.8229, 'learning_rate': 0.0004420689655172414, 'epoch': 0.02}
{'loss': 5.2047, 'learning_rate': 0.00043862068965517246, 'epoch': 0.02}
{'loss': 5.1593, 'learning_rate': 0.00043517241379310346, 'epoch': 0.02}
{'loss': 5.1894, 'learning_rate': 0.00043172413793103446, 'epoch': 0.02}
{'loss': 4.8937, 'learning_rate': 0.0004282758620689655, 'epoch': 0.02}
{'loss': 5.2911, 'learning_rate': 0.0004248275862068965, 'epoch': 0.02}
{'loss': 4.9354, 'learning_rate': 0.0004213793103448276, 'epoch': 0.02}
{'loss': 5.0007, 'learning_rate': 0.00041793103448275863, 'epoch': 0.02}
{'loss': 4.9943, 'learning_rate': 0.0004144827586206897, 'epoch': 0.02}
{'loss': 4.9288, 'learning_rate': 0.0004110344827586207, 'epoch': 0.02}
{'loss': 4.6495, 'learning_rate': 0.00040758620689655174, 'epoch': 0.02}
{'loss': 4.8075, 'learning_rate': 0.00040413793103448274, 'epoch': 0.02}
{'loss': 5.047, 'learning_rate': 0.0004006896551724138, 'epoch': 0.02}
{'loss': 4.7298, 'learning_rate': 0.0003972413793103448, 'epoch': 0.02}
{'loss': 5.0614, 'learning_rate': 0.00039379310344827585, 'epoch': 0.02}
{'loss': 4.8462, 'learning_rate': 0.0003903448275862069, 'epoch': 0.02}
{'loss': 4.9027, 'learning_rate': 0.00038689655172413796, 'epoch': 0.02}
{'loss': 4.8653, 'learning_rate': 0.00038344827586206897, 'epoch': 0.02}
{'loss': 5.0492, 'learning_rate': 0.00038, 'epoch': 0.02}
{'loss': 5.0055, 'learning_rate': 0.000376551724137931, 'epoch': 0.02}
{'loss': 4.9203, 'learning_rate': 0.0003731034482758621, 'epoch': 0.02}
{'loss': 5.0601, 'learning_rate': 0.0003696551724137931, 'epoch': 0.02}
{'loss': 4.8342, 'learning_rate': 0.00036620689655172413, 'epoch': 0.02}
{'loss': 4.9579, 'learning_rate': 0.0003627586206896552, 'epoch': 0.02}
{'loss': 4.8838, 'learning_rate': 0.00035931034482758624, 'epoch': 0.02}
{'loss': 5.1245, 'learning_rate': 0.00035586206896551725, 'epoch': 0.02}
{'loss': 4.7047, 'learning_rate': 0.0003524137931034483, 'epoch': 0.02}
{'loss': 4.8103, 'learning_rate': 0.0003489655172413793, 'epoch': 0.02}
{'loss': 5.0187, 'learning_rate': 0.00034551724137931036, 'epoch': 0.02}
67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2000/3000 [1:51:07<34:29, 2.07s/it]***** Running Evaluation *****
Num examples = 1070
Batch size = 1
{'eval_loss': 4.907186985015869, 'eval_runtime': 950.0193, 'eval_samples_per_second': 1.126, 'eval_steps_per_second': 1.126, 'epoch': 0.02}
67%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2000/3000 [2:06:57<34:29, 2.07s/itSaving model checkpoint to output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000
/usr/local/lib/python3.8/site-packages/peft/utils/save_and_load.py:148: UserWarning: Could not find a config file in checkpoint/miniCPM-bf16/ - will assume that the vocabulary was not modified.
warnings.warn(
tokenizer config file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/tokenizer_config.json
Special tokens file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/special_tokens_map.json
[2024-02-20 17:08:07,683] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step2000 is about to be saved!
/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[2024-02-20 17:08:07,726] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_model_states.pt
[2024-02-20 17:08:07,726] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_model_states.pt...
[2024-02-20 17:08:07,760] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_model_states.pt.
[2024-02-20 17:08:07,762] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_optim_states.pt...
[2024-02-20 17:08:07,864] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_optim_states.pt.
[2024-02-20 17:08:07,875] [INFO] [engine.py:3417:_save_zero_checkpoint] zero checkpoint saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2000/global_step2000/zero_pp_rank_0_mp_rank_00_optim_states.pt
[2024-02-20 17:08:07,886] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step2000 is ready now!
{'loss': 5.0544, 'learning_rate': 0.00034206896551724136, 'epoch': 0.02}
{'loss': 4.8734, 'learning_rate': 0.0003386206896551724, 'epoch': 0.02}
{'loss': 4.8588, 'learning_rate': 0.00033517241379310347, 'epoch': 0.02}
{'loss': 4.8809, 'learning_rate': 0.0003317241379310345, 'epoch': 0.02}
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{'loss': 4.7724, 'learning_rate': 0.0003248275862068966, 'epoch': 0.02}
{'loss': 4.9533, 'learning_rate': 0.0003213793103448276, 'epoch': 0.02}
{'loss': 4.6215, 'learning_rate': 0.00031793103448275864, 'epoch': 0.02}
{'loss': 5.2978, 'learning_rate': 0.00031448275862068964, 'epoch': 0.02}
{'loss': 4.7191, 'learning_rate': 0.0003110344827586207, 'epoch': 0.02}
{'loss': 4.8991, 'learning_rate': 0.00030758620689655175, 'epoch': 0.02}
{'loss': 5.2957, 'learning_rate': 0.0003041379310344828, 'epoch': 0.02}
{'loss': 5.186, 'learning_rate': 0.0003006896551724138, 'epoch': 0.02}
{'loss': 4.4386, 'learning_rate': 0.00029724137931034486, 'epoch': 0.02}
{'loss': 4.9961, 'learning_rate': 0.00029379310344827586, 'epoch': 0.02}
{'loss': 4.8623, 'learning_rate': 0.0002903448275862069, 'epoch': 0.02}
{'loss': 4.6938, 'learning_rate': 0.0002868965517241379, 'epoch': 0.02}
{'loss': 5.0361, 'learning_rate': 0.000283448275862069, 'epoch': 0.02}
{'loss': 4.8112, 'learning_rate': 0.00028000000000000003, 'epoch': 0.02}
{'loss': 5.0209, 'learning_rate': 0.0002765517241379311, 'epoch': 0.02}
{'loss': 5.0334, 'learning_rate': 0.0002731034482758621, 'epoch': 0.02}
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{'loss': 5.1651, 'learning_rate': 0.0002593103448275862, 'epoch': 0.02}
{'loss': 5.0377, 'learning_rate': 0.0002558620689655172, 'epoch': 0.02}
{'loss': 5.0951, 'learning_rate': 0.0002524137931034483, 'epoch': 0.02}
{'loss': 4.9358, 'learning_rate': 0.0002489655172413793, 'epoch': 0.02}
{'loss': 4.9352, 'learning_rate': 0.00024551724137931037, 'epoch': 0.02}
{'loss': 4.8925, 'learning_rate': 0.0002420689655172414, 'epoch': 0.02}
{'loss': 4.9003, 'learning_rate': 0.00023862068965517242, 'epoch': 0.02}
{'loss': 4.95, 'learning_rate': 0.00023517241379310345, 'epoch': 0.02}
{'loss': 5.0561, 'learning_rate': 0.0002317241379310345, 'epoch': 0.02}
{'loss': 4.9288, 'learning_rate': 0.00022827586206896553, 'epoch': 0.02}
{'loss': 4.6204, 'learning_rate': 0.00022482758620689656, 'epoch': 0.02}
{'loss': 4.7895, 'learning_rate': 0.0002213793103448276, 'epoch': 0.02}
{'loss': 4.6687, 'learning_rate': 0.00021793103448275865, 'epoch': 0.02}
{'loss': 4.8489, 'learning_rate': 0.00021448275862068967, 'epoch': 0.02}
{'loss': 5.0163, 'learning_rate': 0.0002110344827586207, 'epoch': 0.02}
{'loss': 4.7498, 'learning_rate': 0.00020758620689655173, 'epoch': 0.02}
{'loss': 5.071, 'learning_rate': 0.0002041379310344828, 'epoch': 0.02}
{'loss': 4.6028, 'learning_rate': 0.00020068965517241381, 'epoch': 0.02}
{'loss': 5.2132, 'learning_rate': 0.00019724137931034484, 'epoch': 0.02}
{'loss': 5.3498, 'learning_rate': 0.00019379310344827587, 'epoch': 0.02}
{'loss': 4.8354, 'learning_rate': 0.0001903448275862069, 'epoch': 0.02}
{'loss': 4.7165, 'learning_rate': 0.00018689655172413795, 'epoch': 0.02}
{'loss': 5.1768, 'learning_rate': 0.00018344827586206898, 'epoch': 0.02}
{'loss': 4.8243, 'learning_rate': 0.00017999999999999998, 'epoch': 0.02}
{'loss': 4.8655, 'learning_rate': 0.000176551724137931, 'epoch': 0.02}
{'loss': 4.9831, 'learning_rate': 0.00017310344827586207, 'epoch': 0.02}
83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2500/3000 [2:23:04<14:50, 1.78s/it]***** Running Evaluation *****
Num examples = 1070
Batch size = 1
{'eval_loss': 4.84500789642334, 'eval_runtime': 987.628, 'eval_samples_per_second': 1.083, 'eval_steps_per_second': 1.083, 'epoch': 0.02}
83%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 2500/3000 [2:39:31<14:50, 1.78s/itSaving model checkpoint to output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500
/usr/local/lib/python3.8/site-packages/peft/utils/save_and_load.py:148: UserWarning: Could not find a config file in checkpoint/miniCPM-bf16/ - will assume that the vocabulary was not modified.
warnings.warn(
tokenizer config file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/tokenizer_config.json
Special tokens file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/special_tokens_map.json
[2024-02-20 17:40:42,775] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step2500 is about to be saved!
/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[2024-02-20 17:40:42,818] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_model_states.pt
[2024-02-20 17:40:42,818] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_model_states.pt...
[2024-02-20 17:40:42,853] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_model_states.pt.
[2024-02-20 17:40:42,864] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_optim_states.pt...
[2024-02-20 17:40:42,981] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_optim_states.pt.
[2024-02-20 17:40:42,994] [INFO] [engine.py:3417:_save_zero_checkpoint] zero checkpoint saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-2500/global_step2500/zero_pp_rank_0_mp_rank_00_optim_states.pt
[2024-02-20 17:40:43,006] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step2500 is ready now!
{'loss': 5.2281, 'learning_rate': 0.0001696551724137931, 'epoch': 0.02}
{'loss': 4.6762, 'learning_rate': 0.00016620689655172412, 'epoch': 0.02}
{'loss': 5.1642, 'learning_rate': 0.00016275862068965515, 'epoch': 0.02}
{'loss': 4.7472, 'learning_rate': 0.0001593103448275862, 'epoch': 0.02}
{'loss': 5.0087, 'learning_rate': 0.00015586206896551724, 'epoch': 0.02}
{'loss': 4.9628, 'learning_rate': 0.00015241379310344826, 'epoch': 0.02}
{'loss': 5.0469, 'learning_rate': 0.0001489655172413793, 'epoch': 0.02}
{'loss': 4.8538, 'learning_rate': 0.00014551724137931035, 'epoch': 0.02}
{'loss': 5.242, 'learning_rate': 0.00014206896551724138, 'epoch': 0.02}
{'loss': 4.8959, 'learning_rate': 0.0001386206896551724, 'epoch': 0.02}
{'loss': 4.8742, 'learning_rate': 0.00013517241379310343, 'epoch': 0.02}
{'loss': 4.958, 'learning_rate': 0.0001317241379310345, 'epoch': 0.02}
{'loss': 4.9919, 'learning_rate': 0.00012827586206896552, 'epoch': 0.02}
{'loss': 5.1347, 'learning_rate': 0.00012482758620689654, 'epoch': 0.02}
{'loss': 4.6106, 'learning_rate': 0.00012137931034482759, 'epoch': 0.02}
{'loss': 4.7297, 'learning_rate': 0.00011793103448275861, 'epoch': 0.02}
{'loss': 4.8609, 'learning_rate': 0.00011448275862068966, 'epoch': 0.02}
{'loss': 4.9314, 'learning_rate': 0.00011103448275862068, 'epoch': 0.02}
{'loss': 4.845, 'learning_rate': 0.00010758620689655173, 'epoch': 0.02}
{'loss': 4.8214, 'learning_rate': 0.00010413793103448275, 'epoch': 0.02}
{'loss': 4.6403, 'learning_rate': 0.0001006896551724138, 'epoch': 0.02}
{'loss': 4.8334, 'learning_rate': 9.724137931034482e-05, 'epoch': 0.02}
{'loss': 5.0028, 'learning_rate': 9.379310344827587e-05, 'epoch': 0.02}
{'loss': 4.7361, 'learning_rate': 9.03448275862069e-05, 'epoch': 0.02}
{'loss': 4.8325, 'learning_rate': 8.689655172413794e-05, 'epoch': 0.02}
{'loss': 4.5913, 'learning_rate': 8.344827586206896e-05, 'epoch': 0.02}
{'loss': 4.8138, 'learning_rate': 8e-05, 'epoch': 0.02}
92%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 2770/3000 [2:47:46<06:39, 1.74s/it][2024-02-20 17:48:55,031] [INFO] [loss_scaler.py:190:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 131072, but hysteresis is 2. Reducing hysteresis to 1
{'loss': 4.5148, 'learning_rate': 7.689655172413794e-05, 'epoch': 0.02}
{'loss': 4.7834, 'learning_rate': 7.344827586206897e-05, 'epoch': 0.02}
{'loss': 5.0962, 'learning_rate': 7.000000000000001e-05, 'epoch': 0.02}
{'loss': 4.8841, 'learning_rate': 6.655172413793104e-05, 'epoch': 0.02}
{'loss': 5.1609, 'learning_rate': 6.310344827586208e-05, 'epoch': 0.02}
{'loss': 4.8587, 'learning_rate': 5.9655172413793106e-05, 'epoch': 0.02}
{'loss': 4.5196, 'learning_rate': 5.620689655172414e-05, 'epoch': 0.02}
{'loss': 4.9806, 'learning_rate': 5.2758620689655176e-05, 'epoch': 0.02}
{'loss': 4.8975, 'learning_rate': 4.931034482758621e-05, 'epoch': 0.02}
{'loss': 4.6707, 'learning_rate': 4.5862068965517246e-05, 'epoch': 0.03}
{'loss': 4.9858, 'learning_rate': 4.2413793103448274e-05, 'epoch': 0.03}
{'loss': 4.9286, 'learning_rate': 3.896551724137931e-05, 'epoch': 0.03}
{'loss': 4.6976, 'learning_rate': 3.5517241379310344e-05, 'epoch': 0.03}
{'loss': 4.7789, 'learning_rate': 3.206896551724138e-05, 'epoch': 0.03}
{'loss': 5.1196, 'learning_rate': 2.8620689655172414e-05, 'epoch': 0.03}
{'loss': 4.8572, 'learning_rate': 2.517241379310345e-05, 'epoch': 0.03}
{'loss': 5.0144, 'learning_rate': 2.1724137931034484e-05, 'epoch': 0.03}
{'loss': 4.9704, 'learning_rate': 1.827586206896552e-05, 'epoch': 0.03}
{'loss': 4.8064, 'learning_rate': 1.4827586206896552e-05, 'epoch': 0.03}
{'loss': 4.851, 'learning_rate': 1.1379310344827587e-05, 'epoch': 0.03}
{'loss': 4.8067, 'learning_rate': 7.93103448275862e-06, 'epoch': 0.03}
{'loss': 4.562, 'learning_rate': 4.482758620689655e-06, 'epoch': 0.03}
{'loss': 4.8734, 'learning_rate': 1.0344827586206898e-06, 'epoch': 0.03}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [2:54:35<00:00, 1.77s/it]***** Running Evaluation *****
Num examples = 1070
Batch size = 1
{'eval_loss': 4.809491157531738, 'eval_runtime': 978.543, 'eval_samples_per_second': 1.093, 'eval_steps_per_second': 1.093, 'epoch': 0.03}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [3:10:54<00:00, 1.77s/itSaving model checkpoint to output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000
/usr/local/lib/python3.8/site-packages/peft/utils/save_and_load.py:148: UserWarning: Could not find a config file in checkpoint/miniCPM-bf16/ - will assume that the vocabulary was not modified.
warnings.warn(
tokenizer config file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/tokenizer_config.json
Special tokens file saved in output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/special_tokens_map.json
[2024-02-20 18:12:04,484] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step3000 is about to be saved!
/usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
[2024-02-20 18:12:04,524] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_model_states.pt
[2024-02-20 18:12:04,524] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_model_states.pt...
[2024-02-20 18:12:04,556] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_model_states.pt.
[2024-02-20 18:12:04,557] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_optim_states.pt...
[2024-02-20 18:12:04,654] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_optim_states.pt.
[2024-02-20 18:12:04,664] [INFO] [engine.py:3417:_save_zero_checkpoint] zero checkpoint saved output/AdvertiseGenLoRA/20240220145905/tmp-checkpoint-3000/global_step3000/zero_pp_rank_0_mp_rank_00_optim_states.pt
[2024-02-20 18:12:04,675] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step3000 is ready now!
Training completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': 11457.8744, 'train_samples_per_second': 0.262, 'train_steps_per_second': 0.262, 'train_loss': 5.106624983469645, 'epoch': 0.03}
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [3:10:57<00:00, 3.82s/it]
[2024-02-20 18:12:13,846] [INFO] [launch.py:347:main] Process 198 exits successfully.
model.properties 0 → 100644
View file @ 24eacbc0
# 模型编码
modelCode=622
# 模型名称
modelName=minicpm_classify_pytorch
# 模型描述
modelDescription=基于源MiniCPM修改的分类算法，可用于情感分类等场景，2B参数量效果碾压Mistral-7B。
# 应用场景
appScenario=推理,训练,制造,广媒,金融,能源,医疗,家居,教育
# 框架类型
frameType=pytorch
model/configuration_minicpm.py 0 → 100644
View file @ 24eacbc0
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MiniCPM model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class MiniCPMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MiniCPM-7B.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MiniCPMModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
experimental feature, subject to breaking API changes in future versions.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import MiniCPMModel, MiniCPMConfig
>>> # Initializing a MiniCPM minicpm-7b style configuration
>>> configuration = MiniCPMConfig()
>>> # Initializing a model from the minicpm-7b style configuration
>>> model = MiniCPMModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "minicpm"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=True,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
scale_emb=1,
dim_model_base=1,
scale_depth=1,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.scale_emb = scale_emb
self.dim_model_base = dim_model_base
self.scale_depth = scale_depth
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
try:
import flash_attn
self._attn_implementation = "flash_attention_2"
except:
pass
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
model/modeling_minicpm.py 0 → 100644
View file @ 24eacbc0
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch MiniCPM model."""
import math
import warnings
from typing import List, Optional, Tuple, Union, Dict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import (
AttentionMaskConverter,
_prepare_4d_attention_mask,
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
)
from transformers.utils.import_utils import is_torch_fx_available
from ..finetune import load_tokenizer
from .configuration_minicpm import MiniCPMConfig
import re
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
except:
pass
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
if not is_torch_greater_or_equal_than_1_13:
import torch.fx
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MiniCPMConfig"
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
warnings.warn(
"Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
)
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
warnings.warn(
"Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
)
return AttentionMaskConverter._make_causal_mask(
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
)
# @torch.jit.script # type: ignore
def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
old_dtype = hidden.dtype
variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
return hidden * weight
class MiniCPMRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MiniCPMRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
class MiniCPMRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
# seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
"""MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
"""MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
# cos = cos[position_ids].unsqueeze(unsqueeze_dim)
# sin = sin[position_ids].unsqueeze(unsqueeze_dim)
# q_embed = (q * cos) + (rotate_half(q) * sin)
# k_embed = (k * cos) + (rotate_half(k) * sin)
orig_dtype = k.dtype
cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
q_fp32 = q.to(dtype=torch.float32, device=q.device)
k_fp32 = k.to(dtype=torch.float32, device=k.device)
q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
class MiniCPMMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
if self.config.pretraining_tp > 1:
slice = self.intermediate_size // self.config.pretraining_tp
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
gate_proj = torch.cat(
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
)
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
down_proj = [
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
]
down_proj = sum(down_proj)
else:
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class MiniCPMAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
self._init_rope()
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = MiniCPMRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = hidden_states.size()
if self.config.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
query_states = torch.cat(query_states, dim=-1)
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
key_states = torch.cat(key_states, dim=-1)
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
"with a layer index."
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
else:
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class MiniCPMFlashAttention2(MiniCPMAttention):
"""
MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# MiniCPMFlashAttention2 attention does not support output_attentions
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
output_attentions = False
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (MiniCPMRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
# Handle the case where the model is quantized
if hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = self._flash_attention_forward(
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def _flash_attention_forward(
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
causal = self.is_causal and query_length != 1
# Contains at least one padding token in the sequence
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
query_states, key_states, value_states, attention_mask, query_length
)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
attn_output = flash_attn_func(
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
)
return attn_output
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
value_layer = index_first_axis(
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
if query_length == kv_seq_len:
query_layer = index_first_axis(
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(
batch_size + 1, dtype=torch.int32, device=query_layer.device
) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q,
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
class MiniCPMSdpaAttention(MiniCPMAttention):
"""
MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from MiniCPMAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal=self.is_causal and attention_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
MINICPM_ATTENTION_CLASSES = {
"eager": MiniCPMAttention,
"flash_attention_2": MiniCPMFlashAttention2,
"sdpa": MiniCPMSdpaAttention,
}
class MiniCPMDecoderLayer(nn.Module):
def __init__(self, config: MiniCPMConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = MiniCPMMLP(config)
self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.scale_depth = config.scale_depth
self.num_hidden_layers = config.num_hidden_layers
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
**kwargs,
)
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
MINICPM_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MiniCPMConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
MINICPM_START_DOCSTRING,
)
class MiniCPMPreTrainedModel(PreTrainedModel):
config_class = MiniCPMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MiniCPMDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
MINICPM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
MINICPM_START_DOCSTRING,
)
class MiniCPMModel(MiniCPMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
Args:
config: MiniCPMConfig
"""
def __init__(self, config: MiniCPMConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self._use_sdpa = config._attn_implementation == "sdpa"
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
past_key_values_length = 0
if use_cache:
use_legacy_cache = not isinstance(past_key_values, Cache)
if use_legacy_cache:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_length)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self._use_sdpa and not output_attentions:
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
# embed positions
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = None
if use_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = MiniCPMModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, MiniCPMForCausalLM
>>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.pretraining_tp > 1:
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
logits = torch.cat(logits, dim=-1)
else:
logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
logits = logits.float()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
max_cache_length = past_key_values.get_max_length()
else:
cache_length = past_length = past_key_values[0][0].shape[2]
max_cache_length = None
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
if (
max_cache_length is not None
and attention_mask is not None
and cache_length + input_ids.shape[1] > max_cache_length
):
attention_mask = attention_mask[:, -max_cache_length:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@torch.inference_mode()
def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
**kwargs):
if history is None:
history = []
if logits_processor:
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
else:
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
history.append({"role": role, "content": query})
history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
outputs = self.generate(**inputs, **gen_kwargs)
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
response = tokenizer.decode(outputs)
pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
matches = pattern.findall(response)
if len(matches) > 0:
response = matches[0]
history.append({"role": "assistant", "content": response})
return response, history
@add_start_docstrings(
"""
The MiniCPM Model transformer with a sequence classification head on top (linear layer).
[`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
MINICPM_START_DOCSTRING,
)
class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tokenizer = load_tokenizer(model_path='checkpoint/miniCPM-bf16/')
self.num_labels = config.num_labels
self.model = MiniCPMModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
logits.device
)
else:
sequence_lengths = -1
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = F.one_hot(labels, self.num_labels)# for classify
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
modeling_minicpm.py 0 → 100644
View file @ 24eacbc0
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch MiniCPM model."""
import math
import warnings
from typing import List, Optional, Tuple, Union, Dict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import (
AttentionMaskConverter,
_prepare_4d_attention_mask,
_prepare_4d_causal_attention_mask,
_prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
is_flash_attn_greater_or_equal_2_10,
logging,
replace_return_docstrings,
)
from transformers.utils.import_utils import is_torch_fx_available
from transformers import AutoTokenizer
from .configuration_minicpm import MiniCPMConfig
import re
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
except:
pass
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path='checkpoint/miniCPM-bf16/', trust_remote_code=True) #'checkpoint/miniCPM-bf16/'
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
if not is_torch_greater_or_equal_than_1_13:
import torch.fx
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MiniCPMConfig"
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
warnings.warn(
"Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
)
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
def _make_causal_mask(
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
warnings.warn(
"Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
)
return AttentionMaskConverter._make_causal_mask(
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
)
# @torch.jit.script # type: ignore
def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
old_dtype = hidden.dtype
variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
return hidden * weight
class MiniCPMRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MiniCPMRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
class MiniCPMRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(
# seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
"""MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
t = t / self.scaling_factor
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
"""MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
# cos = cos[position_ids].unsqueeze(unsqueeze_dim)
# sin = sin[position_ids].unsqueeze(unsqueeze_dim)
# q_embed = (q * cos) + (rotate_half(q) * sin)
# k_embed = (k * cos) + (rotate_half(k) * sin)
orig_dtype = k.dtype
cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
q_fp32 = q.to(dtype=torch.float32, device=q.device)
k_fp32 = k.to(dtype=torch.float32, device=k.device)
q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
class MiniCPMMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
if self.config.pretraining_tp > 1:
slice = self.intermediate_size // self.config.pretraining_tp
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
gate_proj = torch.cat(
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
)
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
down_proj = [
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
]
down_proj = sum(down_proj)
else:
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class MiniCPMAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
self._init_rope()
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = MiniCPMRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = hidden_states.size()
if self.config.pretraining_tp > 1:
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
query_states = torch.cat(query_states, dim=-1)
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
key_states = torch.cat(key_states, dim=-1)
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if self.layer_idx is None:
raise ValueError(
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
"with a layer index."
)
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
else:
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class MiniCPMFlashAttention2(MiniCPMAttention):
"""
MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding tokens in case the input contains any of them.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# MiniCPMFlashAttention2 attention does not support output_attentions
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
# overwrite attention_mask with padding_mask
attention_mask = kwargs.pop("padding_mask")
output_attentions = False
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
dropout_rate = self.attention_dropout if self.training else 0.0
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (MiniCPMRMSNorm handles it correctly)
input_dtype = query_states.dtype
if input_dtype == torch.float32:
# Handle the case where the model is quantized
if hasattr(self.config, "_pre_quantization_dtype"):
target_dtype = self.config._pre_quantization_dtype
else:
target_dtype = self.q_proj.weight.dtype
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
value_states = value_states.to(target_dtype)
attn_output = self._flash_attention_forward(
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def _flash_attention_forward(
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
):
"""
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
first unpad the input, then computes the attention scores and pad the final attention scores.
Args:
query_states (`torch.Tensor`):
Input query states to be passed to Flash Attention API
key_states (`torch.Tensor`):
Input key states to be passed to Flash Attention API
value_states (`torch.Tensor`):
Input value states to be passed to Flash Attention API
attention_mask (`torch.Tensor`):
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
position of padding tokens and 1 for the position of non-padding tokens.
dropout (`int`, *optional*):
Attention dropout
softmax_scale (`float`, *optional*):
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
"""
if not self._flash_attn_uses_top_left_mask:
causal = self.is_causal
else:
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
causal = self.is_causal and query_length != 1
# Contains at least one padding token in the sequence
if attention_mask is not None:
batch_size = query_states.shape[0]
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
query_states, key_states, value_states, attention_mask, query_length
)
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
attn_output_unpad = flash_attn_varlen_func(
query_states,
key_states,
value_states,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_in_batch_q,
max_seqlen_k=max_seqlen_in_batch_k,
dropout_p=dropout,
softmax_scale=softmax_scale,
causal=causal,
)
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
else:
attn_output = flash_attn_func(
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
)
return attn_output
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
key_layer = index_first_axis(
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
value_layer = index_first_axis(
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
)
if query_length == kv_seq_len:
query_layer = index_first_axis(
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
)
cu_seqlens_q = cu_seqlens_k
max_seqlen_in_batch_q = max_seqlen_in_batch_k
indices_q = indices_k
elif query_length == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(
batch_size + 1, dtype=torch.int32, device=query_layer.device
) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_layer = query_layer.squeeze(1)
else:
# The -q_len: slice assumes left padding.
attention_mask = attention_mask[:, -query_length:]
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
return (
query_layer,
key_layer,
value_layer,
indices_q,
(cu_seqlens_q, cu_seqlens_k),
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
)
class MiniCPMSdpaAttention(MiniCPMAttention):
"""
MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from MiniCPMAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
logger.warning_once(
"MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and attention_mask is not None:
query_states = query_states.contiguous()
key_states = key_states.contiguous()
value_states = value_states.contiguous()
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
is_causal=self.is_causal and attention_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
MINICPM_ATTENTION_CLASSES = {
"eager": MiniCPMAttention,
"flash_attention_2": MiniCPMFlashAttention2,
"sdpa": MiniCPMSdpaAttention,
}
class MiniCPMDecoderLayer(nn.Module):
def __init__(self, config: MiniCPMConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = MiniCPMMLP(config)
self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.scale_depth = config.scale_depth
self.num_hidden_layers = config.num_hidden_layers
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*):
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
**kwargs,
)
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
MINICPM_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MiniCPMConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
MINICPM_START_DOCSTRING,
)
class MiniCPMPreTrainedModel(PreTrainedModel):
config_class = MiniCPMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MiniCPMDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
_supports_cache_class = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
MINICPM_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance;
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
MINICPM_START_DOCSTRING,
)
class MiniCPMModel(MiniCPMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
Args:
config: MiniCPMConfig
"""
def __init__(self, config: MiniCPMConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self._use_sdpa = config._attn_implementation == "sdpa"
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
past_key_values_length = 0
if use_cache:
use_legacy_cache = not isinstance(past_key_values, Cache)
if use_legacy_cache:
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
past_key_values_length = past_key_values.get_usable_length(seq_length)
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
)
position_ids = position_ids.unsqueeze(0)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
if self._use_flash_attention_2:
# 2d mask is passed through the layers
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
elif self._use_sdpa and not output_attentions:
# output_attentions=True can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
else:
# 4d mask is passed through the layers
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
# embed positions
hidden_states = inputs_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = None
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = None
if use_cache:
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = MiniCPMModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, MiniCPMForCausalLM
>>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.pretraining_tp > 1:
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
logits = torch.cat(logits, dim=-1)
else:
logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
logits = logits.float()
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values is not None:
if isinstance(past_key_values, Cache):
cache_length = past_key_values.get_seq_length()
past_length = past_key_values.seen_tokens
max_cache_length = past_key_values.get_max_length()
else:
cache_length = past_length = past_key_values[0][0].shape[2]
max_cache_length = None
# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
# input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
# input_ids based on the past_length.
elif past_length < input_ids.shape[1]:
input_ids = input_ids[:, past_length:]
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
if (
max_cache_length is not None
and attention_mask is not None
and cache_length + input_ids.shape[1] > max_cache_length
):
attention_mask = attention_mask[:, -max_cache_length:]
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past_key_values:
position_ids = position_ids[:, -input_ids.shape[1] :]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"position_ids": position_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
}
)
return model_inputs
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_past
@torch.inference_mode()
def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
**kwargs):
if history is None:
history = []
if logits_processor:
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
else:
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
history.append({"role": role, "content": query})
history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
outputs = self.generate(**inputs, **gen_kwargs)
outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
response = tokenizer.decode(outputs)
pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
matches = pattern.findall(response)
if len(matches) > 0:
response = matches[0]
history.append({"role": "assistant", "content": response})
return response, history
@add_start_docstrings(
"""
The MiniCPM Model transformer with a sequence classification head on top (linear layer).
[`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
MINICPM_START_DOCSTRING,
)
class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tokenizer = tokenizer
self.num_labels = config.num_labels # if cls=n: self.num_labels = n
self.model = MiniCPMModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.embed_tokens
def set_input_embeddings(self, value):
self.model.embed_tokens = value
@add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
# if self.config.pad_token_id is None and batch_size != 1:
# raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
logits.device
)
else:
sequence_lengths = -1
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = [self.tokenizer.decode([sub_value for sub_value in sub_array if sub_value != -100]) for sub_array in labels.cpu().numpy()]
labels = [int(item) for item in labels]
labels = torch.LongTensor(labels)
labels = F.one_hot(labels, self.num_labels)# for classify
labels = labels.view(-1, self.num_labels)
labels = torch.tensor(labels, dtype=torch.float16)
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_type == "single_label_classification":
pooled_logits = F.softmax(pooled_logits.view(-1, self.num_labels), dim=1)
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits, labels)
# loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
# print(pooled_logits.argmax(1), labels.argmax(1))
elif self.config.problem_type == "multi_label_classification":
pooled_logits = F.softmax(pooled_logits.view(-1, self.num_labels), dim=1)
# loss_fct = BCEWithLogitsLoss()
loss_fct = CrossEntropyLoss()
loss = loss_fct(pooled_logits, labels)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
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