forked from phoenix-oss/llama-stack-mirror
refactor: move all llama code to models/llama out of meta reference (#1887)
# What does this PR do? Move around bits. This makes the copies from llama-models _much_ easier to maintain and ensures we don't entangle meta-reference specific tidbits into llama-models code even by accident. Also, kills the meta-reference-quantized-gpu distro and rolls quantization deps into meta-reference-gpu. ## Test Plan ``` LLAMA_MODELS_DEBUG=1 \ with-proxy llama stack run meta-reference-gpu \ --env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \ --env INFERENCE_CHECKPOINT_DIR=<DIR> \ --env MODEL_PARALLEL_SIZE=4 \ --env QUANTIZATION_TYPE=fp8_mixed ``` Start a server with and without quantization. Point integration tests to it using: ``` pytest -s -v tests/integration/inference/test_text_inference.py \ --stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct ```
This commit is contained in:
Ashwin Bharambe
GitHub
parent
c52ccc4bbd
commit
530d4bdfe1
85 changed files with 1267 additions and 1683 deletions
313
llama_stack/models/llama/llama4/generation.py
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313
llama_stack/models/llama/llama4/generation.py
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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 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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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 ..checkpoint import maybe_reshard_state_dict
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from ..datatypes import GenerationResult, QuantizationMode
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from .args import ModelArgs
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from .chat_format import ChatFormat, RawContent, RawMessage
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from .datatypes import LLMInput, MaskedEmbedding, TransformerInput
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from .model import Transformer
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from .tokenizer import Tokenizer
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torch.serialization.add_safe_globals([io.BytesIO, codecs.encode])
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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[QuantizationMode] = 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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ckpt_paths = sorted(Path(ckpt_dir).glob("*.pth"))
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assert len(ckpt_paths) > 0, f"no checkpoint files found in {ckpt_dir}"
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print(f"Loading a checkpoint (shards={len(ckpt_paths)}, current-mp-size={world_size})")
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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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state_dict = maybe_reshard_state_dict(
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ckpt_paths,
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n_kv_heads=model_args.n_kv_heads if model_args.n_kv_heads else model_args.n_heads,
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moe_num_experts=model_args.moe_args.num_experts,
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)
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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(state_dict, strict=False)
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print("Done...")
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model = convert_to_quantized_model(model, ckpt_dir, quantization_mode)
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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(state_dict, 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_inputs: List[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[List[GenerationResult], None, None]:
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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:
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cprint("Input to model:\n", "yellow")
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for inp in llm_inputs:
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cprint(self.tokenizer.decode(inp.tokens), "grey")
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prompt_tokens = [inp.tokens for inp in llm_inputs]
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bsz = len(llm_inputs)
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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 in range(max_prompt_len):
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results = []
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for j, t in enumerate(tokens[:, i]):
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results.append(
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GenerationResult(
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token=t.item(),
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text=self.tokenizer.decode([t.item()]),
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source="input",
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logprobs=(token_logprobs[j, i : i + 1].tolist() if logprobs else None),
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batch_idx=j,
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finished=False,
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ignore_token=t.item() == pad_id,
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)
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)
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yield results
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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 any(inp.images is not None and len(inp.images) > 0 for inp in llm_inputs):
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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 = [inp.images if inp.images is not None else [] for inp in llm_inputs]
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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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results = []
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for idx, t in enumerate(next_token):
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results.append(
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GenerationResult(
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token=t.item(),
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text=self.tokenizer.decode([t.item()]),
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source="output",
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logprobs=(token_logprobs[idx, cur_pos : cur_pos + 1].tolist() if logprobs else None),
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batch_idx=idx,
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finished=eos_reached[idx],
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ignore_token=cur_pos < len(prompt_tokens[idx]),
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)
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)
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yield results
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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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contents: List[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[List[GenerationResult], None, None]:
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llm_inputs = [self.formatter.encode_content(c) for c in contents]
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for result in self.generate(
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llm_inputs=llm_inputs,
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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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yield result
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if all(r.finished for r in result):
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break
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def chat_completion(
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self,
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messages_batch: List[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[List[GenerationResult], None, None]:
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llm_inputs = [self.formatter.encode_dialog_prompt(messages) for messages in messages_batch]
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for result in self.generate(
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llm_inputs=llm_inputs,
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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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yield result
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if all(r.finished for r in result):
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break
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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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