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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@ -4,17 +4,13 @@
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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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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
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import json
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import logging
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import math
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import os
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import sys
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import time
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from pathlib import Path
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from typing import Generator, List, Optional, Tuple, Union
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from typing import Callable, Generator, Optional, Union
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import torch
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import torch.nn.functional as F
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@ -23,27 +19,16 @@ from fairscale.nn.model_parallel.initialize import (
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initialize_model_parallel,
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model_parallel_is_initialized,
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)
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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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ResponseFormat,
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ResponseFormatType,
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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.log import get_logger
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from llama_stack.models.llama.datatypes import Model
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from llama_stack.models.llama.llama3.chat_format import ChatFormat, LLMInput
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from llama_stack.models.llama.llama3.tokenizer import Tokenizer
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from llama_stack.models.llama.sku_list import resolve_model
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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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)
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from ..common import TokenResult, model_checkpoint_dir
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from ..config import MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
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@ -51,7 +36,7 @@ from .args import ModelArgs
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from .model import Transformer
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from .multimodal.model import CrossAttentionTransformer
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log = logging.getLogger(__name__)
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log = get_logger(__name__, category="inference")
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class Llama3:
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@ -146,7 +131,7 @@ class Llama3:
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if isinstance(config, MetaReferenceQuantizedInferenceConfig):
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if isinstance(config.quantization, Fp8QuantizationConfig):
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from ..quantization.loader import convert_to_fp8_quantized_model
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from .quantization.loader import convert_to_fp8_quantized_model
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# load on CPU in bf16 so that fp8 conversion does not find an
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# unexpected (fp32, e.g.) datatype
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@ -159,7 +144,7 @@ class Llama3:
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model.load_state_dict(state_dict, strict=False)
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model = convert_to_fp8_quantized_model(model, config, ckpt_dir)
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elif isinstance(config.quantization, Int4QuantizationConfig):
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from ..quantization.loader import convert_to_int4_quantized_model
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from .quantization.loader import convert_to_int4_quantized_model
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model = Transformer(model_args)
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model = convert_to_int4_quantized_model(model, model_args, config)
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@ -169,7 +154,7 @@ class Llama3:
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# Add a wrapper for adding hadamard transform for spinquant.
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# This needs to be done after loading the state dict otherwise an error will be raised while
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# loading the state dict.
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from ..quantization.hadamard_utils import (
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from ..hadamard_utils import (
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add_hadamard_transform_for_spinquant,
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)
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@ -222,9 +207,8 @@ class Llama3:
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top_p: float = 0.9,
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logprobs: bool = False,
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echo: bool = False,
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include_stop_token: bool = False,
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print_input_tokens: bool = False,
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logits_processor: Optional["LogitsProcessor"] = None,
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logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
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) -> Generator:
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params = self.model.params
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@ -292,7 +276,7 @@ class Llama3:
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logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
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if logits_processor is not None:
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logits = logits_processor.process_logits(tokens[:, :cur_pos], logits)
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logits = logits_processor(tokens[:, :cur_pos], logits)
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if temperature > 0:
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probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
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@ -336,58 +320,6 @@ class Llama3:
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if all(eos_reached):
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break
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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
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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.model.params.max_seq_len:
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max_gen_len = self.model.params.max_seq_len - 1
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model_input = self.formatter.encode_content(request.content)
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.generate(
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model_input=model_input,
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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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include_stop_token=True,
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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
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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.model.params.max_seq_len:
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max_gen_len = self.model.params.max_seq_len - 1
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temperature, top_p = _infer_sampling_params(sampling_params)
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yield from self.generate(
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model_input=self.formatter.encode_dialog_prompt(
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request.messages,
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request.tool_config.tool_prompt_format,
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),
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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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include_stop_token=True,
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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 sample_top_p(probs, p):
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"""
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@ -412,72 +344,3 @@ def sample_top_p(probs, p):
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next_token = torch.multinomial(probs_sort, num_samples=1)
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next_token = torch.gather(probs_idx, -1, next_token)
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return next_token
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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 process_logits(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 response_format.type != ResponseFormatType.json_schema.value:
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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
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top_p = sampling_params.strategy.top_p
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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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