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Add provider deprecation support; change directory structure (#397)

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* Add provider deprecation support; change directory structure

* fix a couple dangling imports

* move the meta_reference safety dir also
This commit is contained in:
Ashwin Bharambe 2024-11-07 13:04:53 -08:00 • committed by GitHub
parent 36e2538eb0
commit 694c142b89
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import json
import math
import os
import sys
import time
from pathlib import Path
from typing import Generator, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from fairscale.nn.model_parallel.initialize import (
get_model_parallel_rank,
initialize_model_parallel,
model_parallel_is_initialized,
)
from llama_models.llama3.api.args import ModelArgs
from llama_models.llama3.api.chat_format import ChatFormat, ModelInput
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.llama3.reference_impl.model import Transformer
from llama_models.llama3.reference_impl.multimodal.model import (
CrossAttentionTransformer,
)
from llama_models.sku_list import resolve_model
from pydantic import BaseModel
from termcolor import cprint
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.utils.model_utils import model_local_dir
from llama_stack.providers.utils.inference.prompt_adapter import (
augment_content_with_response_format_prompt,
chat_completion_request_to_messages,
)
from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData
from .config import (
Fp8QuantizationConfig,
Int4QuantizationConfig,
MetaReferenceInferenceConfig,
MetaReferenceQuantizedInferenceConfig,
)
def model_checkpoint_dir(model) -> str:
checkpoint_dir = Path(model_local_dir(model.descriptor()))
paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
if not any(p.exists() for p in paths):
checkpoint_dir = checkpoint_dir / "original"
assert checkpoint_dir.exists(), (
f"Could not find checkpoints in: {model_local_dir(model.descriptor())}. "
f"Please download model using `llama download --model-id {model.descriptor()}`"
)
return str(checkpoint_dir)
class TokenResult(BaseModel):
token: int
text: str
logprobs: Optional[List[float]] = None
class Llama:
@staticmethod
def build(
config: Union[
MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
],
):
"""
Build a Llama instance by initializing and loading a model checkpoint.
Note:
This method initializes the distributed process group, sets the device to CUDA,
and loads the pre-trained model and tokenizer.
"""
model = resolve_model(config.model)
if not torch.distributed.is_initialized():
torch.distributed.init_process_group("nccl")
model_parallel_size = config.model_parallel_size
if not model_parallel_is_initialized():
initialize_model_parallel(model_parallel_size)
local_rank = int(os.environ.get("LOCAL_RANK", 0))
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
if config.torch_seed is not None:
torch.manual_seed(config.torch_seed)
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
start_time = time.time()
if config.checkpoint_dir:
ckpt_dir = config.checkpoint_dir
else:
ckpt_dir = model_checkpoint_dir(model)
checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
assert model_parallel_size == len(
checkpoints
), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
ckpt_path = checkpoints[get_model_parallel_rank()]
state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
with open(Path(ckpt_dir) / "params.json", "r") as f:
params = json.loads(f.read())
if "model" in params:
params = params["model"]
model_args: ModelArgs = ModelArgs(
max_seq_len=config.max_seq_len,
max_batch_size=config.max_batch_size,
**params,
)
tokenizer = Tokenizer.get_instance()
assert (
model_args.vocab_size == tokenizer.n_words
), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
if isinstance(config, MetaReferenceQuantizedInferenceConfig):
if isinstance(config.quantization, Fp8QuantizationConfig):
from .quantization.loader import convert_to_fp8_quantized_model
# load on CPU in bf16 so that fp8 conversion does not find an
# unexpected (fp32, e.g.) datatype
torch.set_default_tensor_type(torch.BFloat16Tensor)
if model_args.vision_chunk_size > 0:
model = CrossAttentionTransformer(model_args)
model.setup_cache(model_args.max_batch_size, torch.bfloat16)
else:
model = Transformer(model_args)
model.load_state_dict(state_dict, strict=False)
model = convert_to_fp8_quantized_model(model, config, ckpt_dir)
elif isinstance(config.quantization, Int4QuantizationConfig):
from .quantization.loader import convert_to_int4_quantized_model
model = Transformer(model_args)
model = convert_to_int4_quantized_model(model, model_args, config)
model.load_state_dict(state_dict, strict=True)
if (
model_args.quantization_args is not None
and model_args.quantization_args.spinquant
):
# Add a wrapper for adding hadamard transform for spinquant.
# This needs to be done after loading the state dict otherwise an error will be raised while
# loading the state dict.
from .quantization.hadamard_utils import (
add_hadamard_transform_for_spinquant,
)
add_hadamard_transform_for_spinquant(model)
else:
raise NotImplementedError(
"Currently int4 and fp8 are the only supported quantization methods."
)
else:
if torch.cuda.is_bf16_supported():
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
else:
torch.set_default_tensor_type(torch.cuda.HalfTensor)
if model_args.vision_chunk_size > 0:
model = CrossAttentionTransformer(model_args)
model.setup_cache(model_args.max_batch_size, torch.bfloat16)
else:
model = Transformer(model_args)
model.load_state_dict(state_dict, strict=False)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return Llama(model, tokenizer, model_args)
def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):
self.args = args
self.model = model
self.tokenizer = tokenizer
self.formatter = ChatFormat(tokenizer)
@torch.inference_mode()
def generate(
self,
model_input: ModelInput,
max_gen_len: int,
temperature: float = 0.6,
top_p: float = 0.9,
logprobs: bool = False,
echo: bool = False,
include_stop_token: bool = False,
print_input_tokens: bool = False,
logits_processor: Optional["LogitsProcessor"] = None,
) -> Generator:
params = self.model.params
if print_input_tokens:
input_tokens = [
self.formatter.vision_token if t == 128256 else t
for t in model_input.tokens
]
cprint("Input to model -> " + self.tokenizer.decode(input_tokens), "red")
prompt_tokens = [model_input.tokens]
bsz = 1
assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
min_prompt_len = min(len(t) for t in prompt_tokens)
max_prompt_len = max(len(t) for t in prompt_tokens)
if max_prompt_len >= params.max_seq_len:
cprint(
f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red"
)
return
total_len = min(max_gen_len + max_prompt_len, params.max_seq_len)
is_vision = isinstance(self.model, CrossAttentionTransformer)
if is_vision:
images = model_input.vision.images if model_input.vision is not None else []
mask = model_input.vision.mask if model_input.vision is not None else []
# the method works for bsz > 1 so add a batch dimension
xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = (
self.model.compute_vision_tokens_masks(
batch_images=[images],
batch_masks=[mask],
total_len=total_len,
)
)
pad_id = self.tokenizer.pad_id
tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
for k, t in enumerate(prompt_tokens):
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
if logprobs:
token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
prev_pos = 0
eos_reached = torch.tensor([False] * bsz, device="cuda")
input_text_mask = tokens != pad_id
if min_prompt_len == total_len:
# TODO(ashwin): unify this branch with the one below and figure out multimodal crap
logits = self.model.forward(tokens, prev_pos)
token_logprobs = -F.cross_entropy(
input=logits.transpose(1, 2),
target=tokens,
reduction="none",
ignore_index=pad_id,
)
stop_tokens = torch.tensor(self.tokenizer.stop_tokens, device="cuda")
for cur_pos in range(min_prompt_len, total_len):
if is_vision:
position_ids = torch.arange(
prev_pos, cur_pos, dtype=torch.long, device="cuda"
)
logits = self.model.forward(
position_ids,
tokens,
cross_attention_masks,
full_text_row_masked_out_mask,
xattn_caches,
)
else:
logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
if logits_processor is not None:
logits = logits_processor.process_logits(tokens[:, :cur_pos], logits)
if temperature > 0:
probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
next_token = sample_top_p(probs, top_p)
else:
next_token = torch.argmax(logits[:, -1], dim=-1)
next_token = next_token.reshape(-1)
# only replace token if prompt has already been generated
next_token = torch.where(
input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
)
tokens[:, cur_pos] = next_token
target = tokens[:, prev_pos + 1 : cur_pos + 1]
if is_vision:
# the logits space (num_classes) is designed to never contain a media_token
# however our input token stream does contain them. we need to nuke them here
# or else the CUDA kernels will crash with an illegal memory access
vision_tokens = [self.tokenizer.special_tokens["<|image|>"], 128256]
masks = [target.eq(t) for t in vision_tokens]
if len(masks) > 1:
mask = torch.logical_or(*masks)
else:
mask = masks[0]
target[mask] = 0
if logprobs:
token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
input=logits.transpose(1, 2),
target=tokens[:, prev_pos + 1 : cur_pos + 1],
reduction="none",
ignore_index=pad_id,
)
eos_reached |= (~input_text_mask[:, cur_pos]) & (
torch.isin(next_token, stop_tokens)
)
yield TokenResult(
token=next_token[0].item(),
text=self.tokenizer.decode(next_token.tolist()),
logprobs=(
token_logprobs[:, cur_pos : cur_pos + 1][0].tolist()
if logprobs
else None
),
)
prev_pos = cur_pos
if all(eos_reached):
break
def completion(
self,
request: CompletionRequest,
) -> Generator:
sampling_params = request.sampling_params
max_gen_len = sampling_params.max_tokens
if (
max_gen_len is None
or max_gen_len == 0
or max_gen_len >= self.model.params.max_seq_len
):
max_gen_len = self.model.params.max_seq_len - 1
content = augment_content_with_response_format_prompt(
request.response_format, request.content
)
model_input = self.formatter.encode_content(content)
yield from self.generate(
model_input=model_input,
max_gen_len=max_gen_len,
temperature=sampling_params.temperature,
top_p=sampling_params.top_p,
logprobs=bool(request.logprobs),
include_stop_token=True,
logits_processor=get_logits_processor(
self.tokenizer,
self.args.vocab_size,
request.response_format,
),
)
def chat_completion(
self,
request: ChatCompletionRequest,
) -> Generator:
messages = chat_completion_request_to_messages(request)
sampling_params = request.sampling_params
max_gen_len = sampling_params.max_tokens
if (
max_gen_len is None
or max_gen_len == 0
or max_gen_len >= self.model.params.max_seq_len
):
max_gen_len = self.model.params.max_seq_len - 1
yield from self.generate(
model_input=self.formatter.encode_dialog_prompt(
messages,
request.tool_prompt_format,
),
max_gen_len=max_gen_len,
temperature=sampling_params.temperature,
top_p=sampling_params.top_p,
logprobs=bool(request.logprobs),
include_stop_token=True,
logits_processor=get_logits_processor(
self.tokenizer,
self.args.vocab_size,
request.response_format,
),
)
def sample_top_p(probs, p):
"""
Perform top-p (nucleus) sampling on a probability distribution.
Args:
probs (torch.Tensor): Probability distribution tensor.
p (float): Probability threshold for top-p sampling.
Returns:
torch.Tensor: Sampled token indices.
Note:
Top-p sampling selects the smallest set of tokens whose cumulative probability mass
exceeds the threshold p. The distribution is renormalized based on the selected tokens.
"""
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token
class LogitsProcessor:
def __init__(self, token_enforcer: TokenEnforcer):
self.token_enforcer = token_enforcer
self.mask: Optional[torch.Tensor] = None
def process_logits(
self, tokens: torch.Tensor, scores: torch.Tensor
) -> torch.Tensor:
token_sequence = tokens[0, :].tolist()
allowed_tokens = self.token_enforcer.get_allowed_tokens(token_sequence)
if self.mask is not None:
self.mask.fill_(-math.inf)
else:
self.mask = torch.full_like(scores, -math.inf)
self.mask[:, :, allowed_tokens] = 0
scores = scores + self.mask
return scores
def get_logits_processor(
tokenizer: Tokenizer,
vocab_size: int,
response_format: Optional[ResponseFormat],
) -> Optional["LogitsProcessor"]:
if response_format is None:
return None
if response_format.type != ResponseFormatType.json_schema.value:
raise ValueError(f"Unsupported response format type {response_format.type}")
parser = JsonSchemaParser(response_format.json_schema)
data = TokenEnforcerTokenizerData(
_build_regular_tokens_list(tokenizer, vocab_size),
tokenizer.decode,
tokenizer.stop_tokens,
)
token_enforcer = TokenEnforcer(data, parser)
return LogitsProcessor(token_enforcer)
def _build_regular_tokens_list(
tokenizer: Tokenizer, vocab_size: int
) -> List[Tuple[int, str, bool]]:
token_0 = tokenizer.encode("0", bos=False, eos=False)[-1]
regular_tokens = []
special_token_ids = set(tokenizer.special_tokens.values())
for token_idx in range(vocab_size):
if token_idx in special_token_ids:
continue
# We prepend token 0 and skip the first letter of the result to get a space if the token is a start word.
decoded_after_0 = tokenizer.decode([token_0, token_idx])[1:]
decoded_regular = tokenizer.decode([token_idx])
is_word_start_token = len(decoded_after_0) > len(decoded_regular)
regular_tokens.append((token_idx, decoded_after_0, is_word_start_token))
return regular_tokens