forked from phoenix-oss/llama-stack-mirror
refactor: move generation.py to llama3
This commit is contained in:
Ashwin Bharambe
parent
725423c95c
commit
02066591b8
4 changed files with 46 additions and 35 deletions
|
|
@ -0,0 +1,483 @@
|
|||
# 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 logging
|
||||
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 lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData
|
||||
|
||||
from llama_stack.apis.inference import (
|
||||
Fp8QuantizationConfig,
|
||||
Int4QuantizationConfig,
|
||||
ResponseFormat,
|
||||
ResponseFormatType,
|
||||
)
|
||||
from llama_stack.models.llama.datatypes import (
|
||||
GreedySamplingStrategy,
|
||||
Model,
|
||||
SamplingParams,
|
||||
TopPSamplingStrategy,
|
||||
)
|
||||
from llama_stack.models.llama.llama3.chat_format import ChatFormat, LLMInput
|
||||
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
|
||||
from llama_stack.models.llama.sku_list import resolve_model
|
||||
from llama_stack.providers.utils.inference.prompt_adapter import (
|
||||
ChatCompletionRequestWithRawContent,
|
||||
CompletionRequestWithRawContent,
|
||||
)
|
||||
|
||||
from ..common import TokenResult, model_checkpoint_dir
|
||||
from ..config import MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
|
||||
from .args import ModelArgs
|
||||
from .model import Transformer
|
||||
from .multimodal.model import CrossAttentionTransformer
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Llama3:
|
||||
@staticmethod
|
||||
def build(
|
||||
config: Union[MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig],
|
||||
model_id: str,
|
||||
llama_model: Model,
|
||||
):
|
||||
"""
|
||||
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.
|
||||
"""
|
||||
if "DEVICE" in os.environ:
|
||||
device = os.environ.get("DEVICE")
|
||||
if device == "cuda":
|
||||
assert torch.cuda.is_available(), "PyTorch CUDA backend not available"
|
||||
if device == "xpu":
|
||||
assert torch.xpu.is_available(), "PyTorch XPU backend not available"
|
||||
else:
|
||||
if torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
elif torch.xpu.is_available():
|
||||
device = "xpu"
|
||||
else:
|
||||
device = "cpu"
|
||||
log.info(f"Using {device} device")
|
||||
|
||||
llama_model_id = llama_model.core_model_id.value
|
||||
if not torch.distributed.is_initialized():
|
||||
if device == "cuda":
|
||||
torch.distributed.init_process_group("nccl")
|
||||
else:
|
||||
torch.distributed.init_process_group("gloo")
|
||||
|
||||
model_parallel_size = llama_model.pth_file_count
|
||||
|
||||
if not model_parallel_is_initialized():
|
||||
initialize_model_parallel(model_parallel_size)
|
||||
|
||||
local_rank = int(os.environ.get("LOCAL_RANK", 0))
|
||||
if device == "cuda":
|
||||
torch.cuda.set_device(local_rank)
|
||||
elif device == "xpu":
|
||||
torch.xpu.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 and config.checkpoint_dir != "null":
|
||||
ckpt_dir = config.checkpoint_dir
|
||||
else:
|
||||
resolved_model = resolve_model(model_id)
|
||||
if resolved_model is None:
|
||||
# if the model is not a native llama model, get the default checkpoint_dir based on model id
|
||||
ckpt_dir = model_checkpoint_dir(model_id)
|
||||
else:
|
||||
# if the model is a native llama model, get the default checkpoint_dir based on model core_model_id value
|
||||
ckpt_dir = model_checkpoint_dir(resolved_model.descriptor())
|
||||
|
||||
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 device == "cuda":
|
||||
if torch.cuda.is_bf16_supported():
|
||||
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
|
||||
else:
|
||||
torch.set_default_tensor_type(torch.cuda.HalfTensor)
|
||||
else:
|
||||
torch.set_default_device(device)
|
||||
if device == "xpu" and torch.xpu.is_bf16_supported():
|
||||
torch.set_default_dtype(torch.bfloat16)
|
||||
else:
|
||||
torch.set_default_dtype(torch.half)
|
||||
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.to(device)
|
||||
|
||||
log.info(f"Loaded in {time.time() - start_time:.2f} seconds")
|
||||
return Llama3(model, tokenizer, model_args, llama_model_id)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: Transformer,
|
||||
tokenizer: Tokenizer,
|
||||
args: ModelArgs,
|
||||
llama_model: str,
|
||||
):
|
||||
self.args = args
|
||||
self.model = model
|
||||
self.tokenizer = tokenizer
|
||||
self.formatter = ChatFormat(tokenizer)
|
||||
self.llama_model = llama_model
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate(
|
||||
self,
|
||||
model_input: LLMInput,
|
||||
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]
|
||||
log.info("Input to model -> " + self.tokenizer.decode(input_tokens))
|
||||
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:
|
||||
log.error(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}")
|
||||
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)
|
||||
for k, t in enumerate(prompt_tokens):
|
||||
tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long)
|
||||
if logprobs:
|
||||
token_logprobs = torch.zeros_like(tokens)
|
||||
|
||||
prev_pos = 0
|
||||
eos_reached = torch.tensor([False] * bsz)
|
||||
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)
|
||||
for cur_pos in range(min_prompt_len, total_len):
|
||||
if is_vision:
|
||||
position_ids = torch.arange(prev_pos, cur_pos, dtype=torch.long)
|
||||
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: CompletionRequestWithRawContent,
|
||||
) -> 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
|
||||
|
||||
model_input = self.formatter.encode_content(request.content)
|
||||
temperature, top_p = _infer_sampling_params(sampling_params)
|
||||
yield from self.generate(
|
||||
model_input=model_input,
|
||||
max_gen_len=max_gen_len,
|
||||
temperature=temperature,
|
||||
top_p=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: ChatCompletionRequestWithRawContent,
|
||||
) -> 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
|
||||
|
||||
temperature, top_p = _infer_sampling_params(sampling_params)
|
||||
yield from self.generate(
|
||||
model_input=self.formatter.encode_dialog_prompt(
|
||||
request.messages,
|
||||
request.tool_config.tool_prompt_format,
|
||||
),
|
||||
max_gen_len=max_gen_len,
|
||||
temperature=temperature,
|
||||
top_p=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
|
||||
|
||||
|
||||
def _infer_sampling_params(sampling_params: SamplingParams):
|
||||
if isinstance(sampling_params.strategy, GreedySamplingStrategy):
|
||||
temperature = 0.0
|
||||
top_p = 1.0
|
||||
elif isinstance(sampling_params.strategy, TopPSamplingStrategy):
|
||||
temperature = sampling_params.strategy.temperature
|
||||
top_p = sampling_params.strategy.top_p
|
||||
else:
|
||||
raise ValueError(f"Unsupported sampling strategy {sampling_params.strategy}")
|
||||
return temperature, top_p
|
||||
Loading…
Add table
Add a link
Reference in a new issue