mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-28 02:53:30 +00:00
use logging instead of prints (#499)
# What does this PR do?
This PR moves all print statements to use logging. Things changed:
- Had to add `await start_trace("sse_generator")` to server.py to
actually get tracing working. else was not seeing any logs
- If no telemetry provider is provided in the run.yaml, we will write to
stdout
- by default, the logs are going to be in JSON, but we expose an option
to configure to output in a human readable way.
This commit is contained in:
Dinesh Yeduguru
GitHub
parent
4e1105e563
commit
6395dadc2b
36 changed files with 234 additions and 163 deletions
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@ -8,6 +8,7 @@
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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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@ -31,7 +32,6 @@ from llama_models.llama3.reference_impl.multimodal.model import (
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)
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from llama_models.sku_list import resolve_model
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from pydantic import BaseModel
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from termcolor import cprint
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from llama_stack.apis.inference import * # noqa: F403
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@ -50,6 +50,8 @@ from .config import (
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MetaReferenceQuantizedInferenceConfig,
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)
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log = logging.getLogger(__name__)
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def model_checkpoint_dir(model) -> str:
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checkpoint_dir = Path(model_local_dir(model.descriptor()))
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@ -185,7 +187,7 @@ class Llama:
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model = Transformer(model_args)
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model.load_state_dict(state_dict, strict=False)
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print(f"Loaded in {time.time() - start_time:.2f} seconds")
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log.info(f"Loaded in {time.time() - start_time:.2f} seconds")
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return Llama(model, tokenizer, model_args, llama_model)
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def __init__(
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@ -221,7 +223,7 @@ class Llama:
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self.formatter.vision_token if t == 128256 else t
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for t in model_input.tokens
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]
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cprint("Input to model -> " + self.tokenizer.decode(input_tokens), "red")
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log.info("Input to model -> " + self.tokenizer.decode(input_tokens))
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prompt_tokens = [model_input.tokens]
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bsz = 1
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@ -231,9 +233,7 @@ class Llama:
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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(
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f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red"
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)
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log.error(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}")
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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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@ -5,6 +5,7 @@
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# the root directory of this source tree.
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import asyncio
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import logging
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from typing import AsyncGenerator, List
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@ -25,6 +26,7 @@ from .config import MetaReferenceInferenceConfig
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from .generation import Llama
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from .model_parallel import LlamaModelParallelGenerator
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log = logging.getLogger(__name__)
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# there's a single model parallel process running serving the model. for now,
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# we don't support multiple concurrent requests to this process.
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SEMAPHORE = asyncio.Semaphore(1)
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@ -49,7 +51,7 @@ class MetaReferenceInferenceImpl(Inference, ModelRegistryHelper, ModelsProtocolP
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# verify that the checkpoint actually is for this model lol
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async def initialize(self) -> None:
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print(f"Loading model `{self.model.descriptor()}`")
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log.info(f"Loading model `{self.model.descriptor()}`")
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if self.config.create_distributed_process_group:
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self.generator = LlamaModelParallelGenerator(self.config)
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self.generator.start()
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@ -11,6 +11,7 @@
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# the root directory of this source tree.
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import json
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import logging
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import multiprocessing
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import os
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import tempfile
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@ -37,6 +38,8 @@ from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
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from .generation import TokenResult
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log = logging.getLogger(__name__)
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class ProcessingMessageName(str, Enum):
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ready_request = "ready_request"
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@ -183,16 +186,16 @@ def retrieve_requests(reply_socket_url: str):
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group=get_model_parallel_group(),
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)
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if isinstance(updates[0], CancelSentinel):
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print("quitting generation loop because request was cancelled")
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log.info(
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"quitting generation loop because request was cancelled"
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)
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break
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if mp_rank_0():
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send_obj(EndSentinel())
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except Exception as e:
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print(f"[debug] got exception {e}")
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import traceback
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log.exception("exception in generation loop")
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traceback.print_exc()
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if mp_rank_0():
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send_obj(ExceptionResponse(error=str(e)))
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@ -252,7 +255,7 @@ def worker_process_entrypoint(
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except StopIteration:
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break
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print("[debug] worker process done")
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log.info("[debug] worker process done")
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def launch_dist_group(
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@ -313,7 +316,7 @@ def start_model_parallel_process(
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request_socket.send(encode_msg(ReadyRequest()))
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response = request_socket.recv()
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print("Loaded model...")
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log.info("Loaded model...")
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return request_socket, process
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@ -361,7 +364,7 @@ class ModelParallelProcessGroup:
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break
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if isinstance(obj, ExceptionResponse):
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print(f"[debug] got exception {obj.error}")
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log.error(f"[debug] got exception {obj.error}")
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raise Exception(obj.error)
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if isinstance(obj, TaskResponse):
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@ -8,14 +8,20 @@
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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 collections
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import logging
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from typing import Optional, Type
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log = logging.getLogger(__name__)
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try:
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import fbgemm_gpu.experimental.gen_ai # noqa: F401
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print("Using efficient FP8 operators in FBGEMM.")
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log.info("Using efficient FP8 operators in FBGEMM.")
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except ImportError:
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print("No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt.")
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log.error(
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"No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt."
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)
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raise
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import torch
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@ -7,6 +7,7 @@
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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 logging
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import os
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from typing import Any, Dict, List, Optional
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@ -21,7 +22,6 @@ from llama_models.llama3.api.args import ModelArgs
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from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
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from llama_models.sku_list import resolve_model
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from termcolor import cprint
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from torch import nn, Tensor
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from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
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@ -30,6 +30,8 @@ from llama_stack.apis.inference import QuantizationType
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from ..config import MetaReferenceQuantizedInferenceConfig
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log = logging.getLogger(__name__)
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def swiglu_wrapper(
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self,
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@ -60,7 +62,7 @@ def convert_to_fp8_quantized_model(
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# Move weights to GPU with quantization
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if llama_model.quantization_format == CheckpointQuantizationFormat.fp8_mixed.value:
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cprint("Loading fp8 scales...", "yellow")
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log.info("Loading fp8 scales...")
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fp8_scales_path = os.path.join(
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checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
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)
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@ -85,7 +87,7 @@ def convert_to_fp8_quantized_model(
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fp8_activation_scale_ub,
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)
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else:
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cprint("Quantizing fp8 weights from bf16...", "yellow")
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log.info("Quantizing fp8 weights from bf16...")
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for block in model.layers:
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if isinstance(block, TransformerBlock):
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if block.layer_id == 0 or block.layer_id == (model.n_layers - 1):
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@ -8,6 +8,7 @@
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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 os
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import shutil
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import sys
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@ -32,6 +33,8 @@ from llama_stack.providers.inline.inference.meta_reference.quantization.fp8_impl
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quantize_fp8,
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)
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log = logging.getLogger(__name__)
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def main(
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ckpt_dir: str,
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@ -102,7 +105,7 @@ def main(
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else:
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torch.set_default_tensor_type(torch.cuda.HalfTensor)
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print(ckpt_path)
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log.info(ckpt_path)
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assert (
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quantized_ckpt_dir is not None
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), "QUantized checkpoint directory should not be None"
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