forked from phoenix-oss/llama-stack-mirror
feat: introduce llama4 support (#1877)
As title says. Details in README, elsewhere.
This commit is contained in:
Ashwin Bharambe
GitHub
parent
23a99a4b22
commit
b8f1561956
61 changed files with 205222 additions and 6439 deletions
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import math
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from typing import Generator, List, Optional, Tuple
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import torch
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from lmformatenforcer import JsonSchemaParser, TokenEnforcer, TokenEnforcerTokenizerData
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from llama_stack.apis.inference import (
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Fp8QuantizationConfig,
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Int4QuantizationConfig,
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JsonSchemaResponseFormat,
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ResponseFormat,
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)
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from llama_stack.models.llama.datatypes import (
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GreedySamplingStrategy,
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Model,
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SamplingParams,
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TopPSamplingStrategy,
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)
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from llama_stack.models.llama.llama3.tokenizer import Tokenizer as Llama3Tokenizer
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from llama_stack.models.llama.llama4.tokenizer import Tokenizer as Llama4Tokenizer
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from llama_stack.providers.utils.inference.prompt_adapter import (
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ChatCompletionRequestWithRawContent,
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CompletionRequestWithRawContent,
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get_default_tool_prompt_format,
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)
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from .common import model_checkpoint_dir
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from .config import MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
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from .inference import resolve_model
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from .llama3.generation import Llama3
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from .llama4.generation import Llama4
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Tokenizer = Llama4Tokenizer | Llama3Tokenizer
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class LogitsProcessor:
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def __init__(self, token_enforcer: TokenEnforcer):
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self.token_enforcer = token_enforcer
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self.mask: Optional[torch.Tensor] = None
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def __call__(self, tokens: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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token_sequence = tokens[0, :].tolist()
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allowed_tokens = self.token_enforcer.get_allowed_tokens(token_sequence)
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if self.mask is not None:
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self.mask.fill_(-math.inf)
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else:
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self.mask = torch.full_like(scores, -math.inf)
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self.mask[:, :, allowed_tokens] = 0
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scores = scores + self.mask
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return scores
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def get_logits_processor(
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tokenizer: Tokenizer,
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vocab_size: int,
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response_format: Optional[ResponseFormat],
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) -> Optional["LogitsProcessor"]:
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if response_format is None:
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return None
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if not isinstance(response_format, JsonSchemaResponseFormat):
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raise ValueError(f"Unsupported response format type {response_format.type}")
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parser = JsonSchemaParser(response_format.json_schema)
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data = TokenEnforcerTokenizerData(
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_build_regular_tokens_list(tokenizer, vocab_size),
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tokenizer.decode,
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tokenizer.stop_tokens,
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)
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token_enforcer = TokenEnforcer(data, parser)
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return LogitsProcessor(token_enforcer)
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def _build_regular_tokens_list(tokenizer: Tokenizer, vocab_size: int) -> List[Tuple[int, str, bool]]:
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token_0 = tokenizer.encode("0", bos=False, eos=False)[-1]
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regular_tokens = []
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special_token_ids = set(tokenizer.special_tokens.values())
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for token_idx in range(vocab_size):
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if token_idx in special_token_ids:
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continue
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# We prepend token 0 and skip the first letter of the result to get a space if the token is a start word.
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decoded_after_0 = tokenizer.decode([token_0, token_idx])[1:]
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decoded_regular = tokenizer.decode([token_idx])
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is_word_start_token = len(decoded_after_0) > len(decoded_regular)
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regular_tokens.append((token_idx, decoded_after_0, is_word_start_token))
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return regular_tokens
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def _infer_sampling_params(sampling_params: SamplingParams):
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if isinstance(sampling_params.strategy, GreedySamplingStrategy):
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temperature = 0.0
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top_p = 1.0
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elif isinstance(sampling_params.strategy, TopPSamplingStrategy):
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temperature = sampling_params.strategy.temperature or 1.0
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top_p = sampling_params.strategy.top_p or 1.0
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else:
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raise ValueError(f"Unsupported sampling strategy {sampling_params.strategy}")
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return temperature, top_p
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def _infer_tool_prompt_format(request: ChatCompletionRequestWithRawContent):
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tool_config = request.tool_config
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if tool_config is not None and tool_config.tool_prompt_format is not None:
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return tool_config.tool_prompt_format
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else:
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return get_default_tool_prompt_format(request.model)
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class Llama4Generator:
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def __init__(
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self,
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config: MetaReferenceInferenceConfig | MetaReferenceQuantizedInferenceConfig,
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model_id: str,
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llama_model: Model,
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):
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if config.checkpoint_dir and config.checkpoint_dir != "null":
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ckpt_dir = config.checkpoint_dir
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else:
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resolved_model = resolve_model(model_id)
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if resolved_model is None:
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# if the model is not a native llama model, get the default checkpoint_dir based on model id
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ckpt_dir = model_checkpoint_dir(model_id)
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else:
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# if the model is a native llama model, get the default checkpoint_dir based on model core_model_id value
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ckpt_dir = model_checkpoint_dir(resolved_model.descriptor())
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if isinstance(config, MetaReferenceQuantizedInferenceConfig):
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if isinstance(config.quantization, Fp8QuantizationConfig):
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quantization_mode = "fp8_mixed"
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elif isinstance(config.quantization, Int4QuantizationConfig):
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quantization_mode = "int4_mixed"
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else:
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raise ValueError(f"Unsupported quantization mode {config.quantization}")
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else:
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quantization_mode = None
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self.inner_generator = Llama4.build(
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ckpt_dir=ckpt_dir,
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max_seq_len=config.max_seq_len,
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max_batch_size=config.max_batch_size,
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world_size=llama_model.pth_file_count,
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quantization_mode=quantization_mode,
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)
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self.tokenizer = self.inner_generator.tokenizer
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self.args = self.inner_generator.args
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self.formatter = self.inner_generator.formatter
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def completion(
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self,
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request: CompletionRequestWithRawContent,
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) -> Generator:
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sampling_params = request.sampling_params or SamplingParams()
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max_gen_len = sampling_params.max_tokens
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if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
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max_gen_len = self.args.max_seq_len - 1
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.inner_generator.generate(
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llm_input=self.formatter.encode_content(request.content),
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max_gen_len=max_gen_len,
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temperature=temperature,
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top_p=top_p,
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logprobs=bool(request.logprobs),
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echo=False,
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logits_processor=get_logits_processor(
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self.tokenizer,
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self.args.vocab_size,
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request.response_format,
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),
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)
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def chat_completion(
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self,
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request: ChatCompletionRequestWithRawContent,
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) -> Generator:
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sampling_params = request.sampling_params or SamplingParams()
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max_gen_len = sampling_params.max_tokens
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if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
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max_gen_len = self.args.max_seq_len - 1
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.inner_generator.generate(
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llm_input=self.formatter.encode_dialog_prompt(request.messages, _infer_tool_prompt_format(request)),
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max_gen_len=max_gen_len,
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temperature=temperature,
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top_p=top_p,
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logprobs=bool(request.logprobs),
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echo=False,
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logits_processor=get_logits_processor(
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self.tokenizer,
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self.args.vocab_size,
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request.response_format,
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),
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)
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class Llama3Generator:
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def __init__(
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self,
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config: MetaReferenceInferenceConfig | MetaReferenceQuantizedInferenceConfig,
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model_id: str,
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llama_model: Model,
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):
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self.inner_generator = Llama3.build(
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config=config,
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model_id=model_id,
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llama_model=llama_model,
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)
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self.tokenizer = self.inner_generator.tokenizer
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self.args = self.inner_generator.args
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self.formatter = self.inner_generator.formatter
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def completion(
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self,
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request: CompletionRequestWithRawContent,
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) -> Generator:
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sampling_params = request.sampling_params or SamplingParams()
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max_gen_len = sampling_params.max_tokens
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if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
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max_gen_len = self.args.max_seq_len - 1
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.inner_generator.generate(
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model_input=self.formatter.encode_content(request.content),
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max_gen_len=max_gen_len,
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temperature=temperature,
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top_p=top_p,
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logprobs=bool(request.logprobs),
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echo=False,
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logits_processor=get_logits_processor(
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self.tokenizer,
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self.args.vocab_size,
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request.response_format,
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),
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)
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def chat_completion(
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self,
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request: ChatCompletionRequestWithRawContent,
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) -> Generator:
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sampling_params = request.sampling_params or SamplingParams()
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max_gen_len = sampling_params.max_tokens
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if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len:
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max_gen_len = self.args.max_seq_len - 1
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.inner_generator.generate(
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model_input=self.formatter.encode_dialog_prompt(request.messages, _infer_tool_prompt_format(request)),
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max_gen_len=max_gen_len,
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temperature=temperature,
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top_p=top_p,
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logprobs=bool(request.logprobs),
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echo=False,
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logits_processor=get_logits_processor(
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self.tokenizer,
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self.args.vocab_size,
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request.response_format,
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),
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)
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