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 codecs
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import io
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import json
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import os
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import sys
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import time
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from enum import Enum
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from pathlib import Path
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from typing import Callable, Generator, List, Optional
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import torch
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import torch.nn.functional as F
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from fairscale.nn.model_parallel.initialize import (
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get_model_parallel_rank,
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initialize_model_parallel,
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model_parallel_is_initialized,
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)
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from termcolor import cprint
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from llama_stack.models.llama.llama4.chat_format import (
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ChatFormat,
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RawContent,
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RawMessage,
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)
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from llama_stack.models.llama.llama4.tokenizer import Tokenizer
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from ..common import TokenResult
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from .args import ModelArgs
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from .datatypes import LLMInput, MaskedEmbedding, TransformerInput
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from .model import Transformer
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torch.serialization.add_safe_globals([io.BytesIO, codecs.encode])
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class QuantizationMode(str, Enum):
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none = "none"
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fp8_mixed = "fp8_mixed"
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int4_mixed = "int4_mixed"
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class Llama4:
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@staticmethod
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def build(
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ckpt_dir: str,
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max_seq_len: int,
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max_batch_size: int,
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world_size: Optional[int] = None,
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quantization_mode: Optional[str] = None,
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seed: int = 1,
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):
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if not torch.distributed.is_initialized():
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torch.distributed.init_process_group("nccl")
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if not model_parallel_is_initialized():
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if world_size is None:
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world_size = int(os.environ.get("WORLD_SIZE", 1))
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initialize_model_parallel(world_size)
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local_rank = int(os.environ.get("LOCAL_RANK", 0))
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torch.cuda.set_device(local_rank)
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torch.manual_seed(seed)
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if local_rank > 0:
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sys.stdout = open(os.devnull, "w")
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start_time = time.time()
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checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
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assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
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assert world_size == len(checkpoints), (
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f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
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)
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with open(Path(ckpt_dir) / "params.json", "r") as f:
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params = json.loads(f.read())
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model_args: ModelArgs = ModelArgs(
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**params,
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max_seq_len=max_seq_len,
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max_batch_size=max_batch_size,
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)
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tokenizer = Tokenizer.get_instance()
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# TODO: params.json should always have correct vocab_size
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if model_args.vocab_size == -1:
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model_args.vocab_size = tokenizer.n_words
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assert model_args.vocab_size == tokenizer.n_words, f"{model_args.vocab_size=} vs. {tokenizer.n_words=} mismatch"
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print("Model args:\n", model_args.model_dump_json(indent=2))
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ckpt_path = checkpoints[get_model_parallel_rank()]
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print(f"Loading checkpoint from {ckpt_dir}...")
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with open(ckpt_path, "rb") as f:
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checkpoint = torch.load(f, map_location="cpu", weights_only=True)
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print("Loaded checkpoint")
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if quantization_mode == QuantizationMode.fp8_mixed or quantization_mode == QuantizationMode.int4_mixed:
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from .quantization.loader import convert_to_quantized_model
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torch.set_default_tensor_type(torch.BFloat16Tensor)
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model = Transformer(model_args)
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print("Loading state dict...")
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model.load_state_dict(checkpoint, strict=False)
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print("Done...")
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model = convert_to_quantized_model(model, ckpt_dir)
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else:
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if torch.cuda.is_bf16_supported():
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torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
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else:
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torch.set_default_tensor_type(torch.cuda.HalfTensor)
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model = Transformer(model_args)
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print("Loading state dict...")
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model.load_state_dict(checkpoint, strict=False)
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print("Done...")
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print(f"Loaded in {time.time() - start_time:.2f} seconds")
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return Llama4(model, tokenizer, model_args)
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def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):
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self.args = args
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self.model = model
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self.tokenizer = tokenizer
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self.formatter = ChatFormat(tokenizer, vision_args=args.vision_args)
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@torch.inference_mode()
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def generate(
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self,
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llm_input: LLMInput,
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temperature: float = 0.6,
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top_p: float = 0.9,
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max_gen_len: Optional[int] = None,
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logprobs: bool = False,
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echo: bool = False,
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print_model_input: bool = False,
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logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
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) -> Generator:
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if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.model.args.max_seq_len:
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max_gen_len = self.model.args.max_seq_len - 1
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params = self.model.args
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print_model_input = print_model_input or os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1"
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if print_model_input and get_model_parallel_rank() == 0:
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tokens_to_print = list(llm_input.tokens)
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cprint(
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"Input to model:\n" + self.tokenizer.decode(tokens_to_print) + "\n",
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"red",
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)
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prompt_tokens = [llm_input.tokens]
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bsz = 1
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assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
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min_prompt_len = min(len(t) for t in prompt_tokens)
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max_prompt_len = max(len(t) for t in prompt_tokens)
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if max_prompt_len >= params.max_seq_len:
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cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red")
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return
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total_len = min(max_gen_len + max_prompt_len, params.max_seq_len)
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pad_id = self.tokenizer.pad_id
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tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
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for k, t in enumerate(prompt_tokens):
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tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
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if logprobs:
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token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
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eos_reached = torch.tensor([False] * bsz, device="cuda")
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input_text_mask = tokens != pad_id
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if echo:
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for i, t in enumerate(llm_input.tokens):
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yield TokenResult(
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token=t,
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text=self.tokenizer.decode([t]),
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logprobs=(token_logprobs[0, i : i + 1].tolist() if logprobs else None),
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)
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stop_tokens = torch.tensor(self.tokenizer.stop_tokens, device="cuda")
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prev_pos = 0
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for cur_pos in range(min_prompt_len, total_len):
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image_embedding = None
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if prev_pos == 0 and llm_input.images is not None and len(llm_input.images) > 0:
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image_mask = tokens[:, prev_pos:cur_pos] == self.tokenizer.special_tokens["<|patch|>"]
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image_mask = image_mask.unsqueeze(-1)
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h = self.model.tok_embeddings(tokens[:, prev_pos:cur_pos])
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image_batch = [llm_input.images]
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image_embedding = MaskedEmbedding(
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embedding=self.model.vision_embeddings(image_batch, image_mask, h),
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mask=image_mask,
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)
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xformer_input = TransformerInput(
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tokens=tokens[:, prev_pos:cur_pos],
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tokens_position=prev_pos,
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image_embedding=image_embedding,
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)
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xformer_output = self.model.forward(xformer_input)
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logits = xformer_output.logits
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if logits_processor is not None:
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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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next_token = sample_top_p(probs, top_p)
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else:
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next_token = torch.argmax(logits[:, -1], dim=-1)
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next_token = next_token.reshape(-1)
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# only replace token if prompt has already been generated
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next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token)
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tokens[:, cur_pos] = next_token
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target = tokens[:, prev_pos + 1 : cur_pos + 1]
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if logprobs:
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token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
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input=logits.transpose(1, 2),
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target=target,
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reduction="none",
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ignore_index=pad_id,
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)
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eos_reached |= (~input_text_mask[:, cur_pos]) & (torch.isin(next_token, stop_tokens))
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yield TokenResult(
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token=next_token[0].item(),
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text=self.tokenizer.decode(next_token.tolist()),
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logprobs=(token_logprobs[:, cur_pos : cur_pos + 1][0].tolist() if logprobs else None),
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)
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prev_pos = cur_pos
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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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content: RawContent,
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temperature: float = 0.6,
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top_p: float = 0.9,
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max_gen_len: Optional[int] = None,
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logprobs: bool = False,
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echo: bool = False,
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) -> Generator:
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llm_input = self.formatter.encode_content(content)
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for result in self.generate(
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llm_input=llm_input,
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temperature=temperature,
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top_p=top_p,
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max_gen_len=max_gen_len,
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logprobs=logprobs,
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echo=echo,
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):
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if result.token in self.tokenizer.stop_tokens:
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break
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yield result
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def chat_completion(
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self,
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messages: List[RawMessage],
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temperature: float = 0.6,
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top_p: float = 0.9,
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max_gen_len: Optional[int] = None,
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logprobs: bool = False,
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echo: bool = False,
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) -> Generator:
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llm_input = self.formatter.encode_dialog_prompt(messages)
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for result in self.generate(
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llm_input=llm_input,
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temperature=temperature,
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top_p=top_p,
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max_gen_len=max_gen_len,
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logprobs=logprobs,
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echo=echo,
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):
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if result.token in self.tokenizer.stop_tokens:
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break
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yield result
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def chat_completion_raw(
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self,
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messages: List[RawMessage],
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temperature: float = 0.6,
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top_p: float = 0.9,
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max_gen_len: Optional[int] = None,
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logprobs: bool = False,
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):
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llm_input = self.formatter.encode_dialog_prompt(messages)
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output_tokens = []
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for result in self.generate(
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llm_input=llm_input,
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temperature=temperature,
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top_p=top_p,
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max_gen_len=max_gen_len,
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logprobs=logprobs,
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):
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output_tokens.append(result.token)
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return llm_input.tokens, output_tokens
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def sample_top_p(probs, p):
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"""
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Perform top-p (nucleus) sampling on a probability distribution.
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Args:
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probs (torch.Tensor): Probability distribution tensor.
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p (float): Probability threshold for top-p sampling.
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Returns:
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torch.Tensor: Sampled token indices.
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Note:
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Top-p sampling selects the smallest set of tokens whose cumulative probability mass
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exceeds the threshold p. The distribution is renormalized based on the selected tokens.
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"""
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probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
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probs_sum = torch.cumsum(probs_sort, dim=-1)
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mask = probs_sum - probs_sort > p
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probs_sort[mask] = 0.0
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probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
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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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