mirror of
https://github.com/meta-llama/llama-stack.git
synced 2026-01-03 09:22:17 +00:00
several fixes
This commit is contained in:
Ashwin Bharambe
parent
e2e2820c9a
commit
53a8086e37
60 changed files with 1006 additions and 1078 deletions
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@ -4,59 +4,37 @@
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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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# 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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# 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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# top-level folder for each specific model found within the models/ directory at
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# the top-level of this source tree.
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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 dataclasses import dataclass
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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 ..datatypes import RawContent, RawMessage, StopReason, ToolPromptFormat
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from ..checkpoint import maybe_reshard_state_dict
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from ..datatypes import GenerationResult, QuantizationMode, RawContent, RawMessage, ToolPromptFormat
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from .args import ModelArgs
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from .chat_format import ChatFormat, LLMInput
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from .model import Transformer
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from .multimodal.model import CrossAttentionTransformer
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from .tokenizer import Tokenizer
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@dataclass
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class CompletionPrediction:
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generation: str
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decoded_tokens: Optional[List[str]] = None
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logprobs: Optional[List[List[float]]] = None
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@dataclass
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class ChatPrediction:
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generation: RawMessage
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decoded_tokens: Optional[List[str]] = None
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logprobs: Optional[List[List[float]]] = None
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@dataclass
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class TokenResult:
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token: int
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text: str
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logprobs: Optional[List[float]] = None
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# TODO: make this completely parallel to the llama4 generation.py file and share common code
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# from llama-models also
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class Llama3:
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@staticmethod
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def build(
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@ -64,7 +42,7 @@ class Llama3:
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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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tokenizer_path: Optional[str] = None,
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quantization_mode: Optional[QuantizationMode] = None,
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seed: int = 1,
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device: str = "cuda",
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):
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@ -101,13 +79,9 @@ class Llama3:
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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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ckpt_path = checkpoints[get_model_parallel_rank()]
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checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=True)
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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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@ -116,40 +90,58 @@ class Llama3:
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max_batch_size=max_batch_size,
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**params,
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)
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if tokenizer_path:
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tokenizer = Tokenizer(model_path=tokenizer_path)
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else:
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tokenizer = Tokenizer.get_instance()
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tokenizer = Tokenizer.get_instance()
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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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)
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assert model_args.vocab_size == tokenizer.n_words
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torch.set_default_device(device)
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if device.type == "cuda":
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if torch.cuda.is_bf16_supported():
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torch.set_default_dtype(torch.bfloat16)
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else:
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torch.set_default_dtype(torch.half)
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elif device.type == "xpu":
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if torch.xpu.is_bf16_supported():
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torch.set_default_dtype(torch.bfloat16)
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else:
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torch.set_default_dtype(torch.half)
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else:
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torch.set_default_dtype(torch.half)
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if model_args.vision_chunk_size > 0:
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from .multimodal.model import CrossAttentionTransformer
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def build_model():
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if model_args.vision_chunk_size > 0:
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model = CrossAttentionTransformer(model_args)
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model.setup_cache(model_args.max_batch_size, device=device, dtype=torch.get_default_dtype())
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else:
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model = Transformer(model_args)
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return model
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model = CrossAttentionTransformer(model_args)
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model.setup_cache(model_args.max_batch_size, torch.get_default_dtype())
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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 = build_model()
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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, device=device)
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torch.set_default_device(device)
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else:
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model = Transformer(model_args)
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model.load_state_dict(checkpoint, strict=True)
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model.to(device)
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print(f"Setting default device to {device}")
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torch.set_default_device(device)
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if device.type == "cuda":
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if torch.cuda.is_bf16_supported():
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torch.set_default_dtype(torch.bfloat16)
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else:
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torch.set_default_dtype(torch.half)
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elif device.type == "xpu":
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if torch.xpu.is_bf16_supported():
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torch.set_default_dtype(torch.bfloat16)
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else:
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torch.set_default_dtype(torch.half)
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model = build_model()
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print("Loading state dict...")
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model.load_state_dict(state_dict, strict=True)
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model.to(device)
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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 Llama(model, tokenizer, model_args)
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return Llama3(model, tokenizer, model_args)
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def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):
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def __init__(self, model: Transformer | CrossAttentionTransformer, 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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@ -158,26 +150,30 @@ class Llama3:
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@torch.inference_mode()
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def generate(
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self,
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model_input: LLMInput,
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max_gen_len: int,
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model_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:
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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.args.max_seq_len:
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max_gen_len = self.args.max_seq_len - 1
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params = self.model.params
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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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tokens_to_print = [self.formatter.vision_token if t == 128256 else t for t in model_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 = [model_input.tokens]
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for inp in model_inputs:
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tokens_to_print = [self.formatter.vision_token if t == 128256 else t for t in inp.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 = [inp.tokens for inp in model_inputs]
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bsz = 1
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bsz = len(model_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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@ -189,18 +185,6 @@ class Llama3:
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total_len = min(max_gen_len + max_prompt_len, params.max_seq_len)
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is_vision = not isinstance(self.model, Transformer)
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if is_vision:
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images = model_input.vision.images if model_input.vision is not None else []
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mask = model_input.vision.mask if model_input.vision is not None else []
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# the method works for bsz > 1 so add a batch dimension
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xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = self.model.compute_vision_tokens_masks(
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batch_images=[images],
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batch_masks=[mask],
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total_len=total_len,
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)
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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)
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for k, t in enumerate(prompt_tokens):
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@ -208,23 +192,45 @@ class Llama3:
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if logprobs:
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token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
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prev_pos = 0
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is_vision = not isinstance(self.model, Transformer)
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if is_vision:
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images = [inp.vision.images if inp.vision is not None else [] for inp in model_inputs]
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mask = [inp.vision.mask if inp.vision is not None else [] for inp in model_inputs]
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xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = self.model.compute_vision_tokens_masks(
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batch_images=images,
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batch_masks=mask,
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total_len=total_len,
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device=tokens.device,
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)
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eos_reached = torch.tensor([False] * bsz)
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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(model_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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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)
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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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if is_vision:
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position_ids = torch.arange(prev_pos, cur_pos, dtype=torch.long)
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text_only_inference = model_input.vision is None
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text_only_inference = all(inp.vision is None for inp in model_inputs)
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logits = self.model.forward(
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position_ids,
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tokens,
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@ -271,155 +277,69 @@ class Llama3:
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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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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 text_completion(
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def completion(
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self,
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content: RawContent,
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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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) -> CompletionPrediction:
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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(content)
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tokens = []
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token_logprobs = []
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decoded_tokens = []
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) -> Generator[List[GenerationResult], None, None]:
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model_inputs = [self.formatter.encode_content(c) for c in contents]
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for result in self.generate(
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model_input=model_input,
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max_gen_len=max_gen_len,
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model_inputs=model_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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tokens.append(result.token)
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if logprobs:
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decoded_tokens.append(result.text)
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token_logprobs.append(result.logprobs)
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generation = self.tokenizer.decode(tokens)
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if logprobs:
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return CompletionPrediction(
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generation=generation,
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logprobs=token_logprobs,
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decoded_tokens=decoded_tokens,
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)
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return CompletionPrediction(generation=generation)
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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: List[RawMessage],
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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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tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
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echo: bool = False,
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) -> ChatPrediction:
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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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tokens = []
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token_logprobs = []
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decoded_tokens = []
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stop_reason = None
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) -> Generator[List[GenerationResult], None, None]:
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model_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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model_input=self.formatter.encode_dialog_prompt(messages, tool_prompt_format),
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max_gen_len=max_gen_len,
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model_inputs=model_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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tokens.append(result.token)
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if result.text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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elif result.text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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if logprobs:
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decoded_tokens.append(result.text)
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token_logprobs.append(result.logprobs)
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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message = self.formatter.decode_assistant_message(tokens, stop_reason)
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if logprobs:
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return ChatPrediction(
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generation=message,
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logprobs=token_logprobs,
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decoded_tokens=decoded_tokens,
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)
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return ChatPrediction(generation=message)
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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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tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
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) -> List[int]:
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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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output_tokens = []
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model_input = self.formatter.encode_dialog_prompt(messages, tool_prompt_format)
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input_tokens = model_input.tokens
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for result in 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,
|
||||
top_p=top_p,
|
||||
logprobs=False,
|
||||
):
|
||||
output_tokens.append(result.token)
|
||||
|
||||
return input_tokens, output_tokens
|
||||
|
||||
def text_completion_raw(
|
||||
self,
|
||||
content: RawContent,
|
||||
temperature: float = 0.6,
|
||||
top_p: float = 0.9,
|
||||
max_gen_len: Optional[int] = None,
|
||||
):
|
||||
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(content)
|
||||
input_tokens = model_input.tokens
|
||||
|
||||
output_tokens = []
|
||||
for result in self.generate(
|
||||
model_input=model_input,
|
||||
max_gen_len=max_gen_len,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
logprobs=False,
|
||||
):
|
||||
output_tokens.append(result.token)
|
||||
|
||||
return input_tokens, output_tokens
|
||||
yield result
|
||||
if all(r.finished for r in result):
|
||||
break
|
||||
|
||||
|
||||
def sample_top_p(probs, p):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue