experimental/whisper/speedup.ipynb

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   "source": [
    "# Whisper benchmark\n",
    "\n",
    "To measure the speedup of `Kernl` on Whisper model, we use `test` set from `librispeech` on a 3090 RTX GPU.\n",
    "\n",
    "We use the following setup:\n",
    "- `openai/whisper-large-v2` weights\n",
    "- beam search with 5 beams (as advised in [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf))\n",
    "- only apply optimization to the decoder, because encoder counts for very little in the end-to-end latency\n",
    "- use CUDA graphs (to remove most of the CPU overhead)\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "There are dependencies required to use `librispeech`."
   ]
  },
  {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\r\n",
      "\u001B[0m"
     ]
    }
   ],
   "source": [
    "! pip install datasets soundfile librosa -q\n",
    "# on Docker, you may want to install libsndfile1-dev through apt"
   ]
  },
  {
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   "execution_count": 2,
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     "text": [
      "Tue Jan 31 05:03:27 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 515.65.01    Driver Version: 515.65.01    CUDA Version: 12.0     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  NVIDIA A100-SXM...  On   | 00000000:06:00.0 Off |                    0 |\r\n",
      "| N/A   37C    P0    50W / 400W |      0MiB / 40960MiB |      0%      Default |\r\n",
      "|                               |                      |             Disabled |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|  No running processes found                                                 |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Import and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "import time\n",
    "\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "from transformers import WhisperForConditionalGeneration, WhisperProcessor\n",
    "\n",
    "from kernl.model_optimization import optimize_model\n",
    "\n",
    "\n",
    "torch.set_float32_matmul_precision(\"high\")\n",
    "# torchdynamo.config.cache_size_limit = 512\n",
    "# torchdynamo.config.dynamic_shapes = True\n",
    "max_len = 50  # we do not expect more than 50 tokens per audio.\n",
    "num_beams = 5\n",
    "model_name = \"openai/whisper-large-v2\"  # \"openai/whisper-tiny\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Load data & model\n",
    "\n",
    "We set a simple function to extract tokens from audios."
   ]
  },
  {
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     "text": [
      "Downloading and preparing dataset librispeech_asr/clean to /root/.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7...\n"
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      "Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
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     "text": [
      "Dataset librispeech_asr downloaded and prepared to /root/.cache/huggingface/datasets/librispeech_asr/clean/2.1.0/cff5df6e7955c80a67f80e27e7e655de71c689e2d2364bece785b972acb37fe7. Subsequent calls will reuse this data.\n"
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   ],
   "source": [
    "# audio_dataset = load_dataset(\"hf-internal-testing/librispeech_asr_dummy\", \"clean\", split=\"validation\")  # small dataset for tests\n",
    "audio_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"test\")\n",
    "\n",
    "\n",
    "def get_tokens(item: dict[str, dict]) -> torch.Tensor:\n",
    "    tensor = processor(item[\"audio\"][\"array\"], return_tensors=\"pt\", sampling_rate=16_000).input_features\n",
    "    return tensor.cuda()\n",
    "\n",
    "\n",
    "processor = WhisperProcessor.from_pretrained(model_name)\n",
    "inputs_warmup = get_tokens(audio_dataset[0])\n",
    "\n",
    "model = WhisperForConditionalGeneration.from_pretrained(model_name).to(\"cuda\").eval()"
   ]
  },
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   "source": [
    "## Baseline\n",
    "\n",
    "Measures is done on mixed precision `FP16` model.\n",
    "We save each model output so we can check the quality impact of the optimizations."
   ]
  },
  {
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   "source": [
    "timings_original = list()\n",
    "transcriptions = list()\n",
    "with torch.inference_mode(), torch.autocast(dtype=torch.float16, cache_enabled=True, device_type=\"cuda\"):\n",
    "    # warmup\n",
    "    model.generate(inputs_warmup, min_length=max_len, max_length=max_len, num_beams=num_beams, do_sample=False)\n",
    "    torch.cuda.synchronize()\n",
    "    for audio in audio_dataset:\n",
    "        inputs = get_tokens(audio)\n",
    "        torch.cuda.synchronize()\n",
    "        start = time.time()\n",
    "        predicted_ids = model.generate(inputs, min_length=1, max_length=max_len, num_beams=num_beams, do_sample=False)\n",
    "        torch.cuda.synchronize()\n",
    "        timings_original.append(time.time() - start)\n",
    "        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[0]\n",
    "        transcriptions.append(transcription)\n",
    "\n",
    "assert len(audio_dataset) == len(transcriptions) == len(timings_original)"
   ]
  },
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   "source": [
    "## Optimized model\n",
    "\n",
    "### Hugging Face implementation\n",
    "\n",
    "First, we fix a small inefficiency in the Hugging Face library.  \n",
    "Basically, it avoids unnecessary encoder tensor (from K/V cache) copies.\n",
    "\n",
    "The impact on speed inference is limited, it's mainly done for memory footprint.  \n",
    "In the past, PyTorch 2.0 nightlies had many memory leaks and this fix was back then mandatory to not get OOM.  \n",
    "FWIW, all memory leaks we have found during our experiments have been fixed in recent PyTorch versions.  "
   ]
  },
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   "source": [
    "@staticmethod\n",
    "def fix_reorder_cache(past, beam_idx):\n",
    "    reordered_past = ()\n",
    "    for layer_past in past:\n",
    "        reordered_past += (\n",
    "            tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],\n",
    "        )\n",
    "    return reordered_past\n",
    "\n",
    "\n",
    "WhisperForConditionalGeneration._reorder_cache = fix_reorder_cache"
   ]
  },
  {
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   "source": [
    "### Kernl optimization\n",
    "\n",
    "Warmup takes around 12 minutes and is mostly spent by PyTorch 2.0 dynamo on the CPU to capture graphs (it is down from 50 min with previous version of Kernl 🤯).\n",
    "We plan to support dynamic shape mode on dynamo, preliminary benchmarks show a 5X faster warmup on Whisper large (basically mostly Triton autotune remains).\n",
    "\n",
    "Note that < 2% outputs are different from the original model.\n",
    "Manual inspection shows that differences are mostly small, many being only 1 or 2 different tokens in the whole transcription.\n",
    "\n",
    "This happens because of rounding on float tensors when 2 tokens get very similar score from Whisper.\n",
    "Keep in mind that with float tensors there is no real commutative operations, order of execution always matters because of rounding even if mathematically 2 expressions are strictly equivalent (which is the case with our optimized kernels). Moreover, because we fuse operations, our kernels tend to be more precise than eager PyTorch because we accumulate in fp32 where PyTorch eager will do it in fp16 most of the time.\n",
    "\n",
    "Anyway, at the end, outputs are very similar and that's what matters :-)"
   ]
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      "time to warmup: 13.42min\n",
      "Kernl speedup: 2.3X (17.1 VS 39.8 min)\n",
      "# different outputs: 37/2620 (1.41%)\n",
      "\n",
      "memory footprint:\n",
      "* allocated: 8.6GB\n",
      "* reserved: 11.3GB\n",
      "* max reserved: 13.6GB\n"
     ]
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    "# uncomment 2 following lines and comment the third one to use vanilla torch.compile instead of Kernl\n",
    "# model.model.decoder.forward_original = model.model.decoder.forward\n",
    "# model.model.decoder.forward = torch.compile(model.model.decoder.forward_original, mode=\"reduce-overhead\")\n",
    "optimize_model(model.model.decoder)\n",
    "nb_diff = 0\n",
    "timings_optimized = list()\n",
    "with torch.inference_mode(), torch.autocast(dtype=torch.float16, cache_enabled=True, device_type=\"cuda\"):\n",
    "    start = time.time()\n",
    "    model.generate(inputs_warmup, min_length=max_len, max_length=max_len, num_beams=num_beams, do_sample=False)\n",
    "    torch.cuda.synchronize()\n",
    "    print(f\"time to warmup: {(time.time() - start)/60:.2f}min\")\n",
    "    for original_modem_transcription, audio in zip(transcriptions, audio_dataset):\n",
    "        inputs = get_tokens(audio)\n",
    "        torch.cuda.synchronize()\n",
    "        start = time.time()\n",
    "        predicted_ids = model.generate(inputs, min_length=1, max_length=max_len, num_beams=num_beams, do_sample=False)\n",
    "        torch.cuda.synchronize()\n",
    "        timings_optimized.append(time.time() - start)\n",
    "        optimized_transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True, normalize=True)[0]\n",
    "        nb_diff += original_modem_transcription != optimized_transcription\n",
    "\n",
    "original_mins = sum(timings_original) / 60\n",
    "optimized_mins = sum(timings_optimized) / 60\n",
    "speedup = original_mins / optimized_mins\n",
    "print(f\"Kernl speedup: {speedup:.1f}X ({optimized_mins:.1f} VS {original_mins:.1f} min)\")\n",
    "print(f\"# different outputs: {nb_diff}/{len(audio_dataset)} ({nb_diff / len(audio_dataset) * 100:.2f}%)\")\n",
    "\n",
    "print(\"\\nmemory footprint:\")\n",
    "print(f\"* allocated: {torch.cuda.memory_allocated(0) / 1024 / 1024 / 1024:.1f}GB\")\n",
    "print(f\"* reserved: {torch.cuda.memory_reserved(0) / 1024 / 1024 / 1024:.1f}GB\")\n",
    "print(f\"* max reserved: {torch.cuda.max_memory_reserved(0) / 1024 / 1024 / 1024:.1f}GB\")"
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