Merge pull request #2 from BlueLabelLabs/feat/Add-HPU-support

Add hpu support

.dockerignore

@@ -0,0 +1 @@
+.graph_dumps

Dockerfile.hpu

@@ -0,0 +1,34 @@
+# Use the official Gaudi Docker image with PyTorch
+FROM vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
+
+# Set environment variables for Habana
+ENV HABANA_VISIBLE_DEVICES=all
+ENV OMPI_MCA_btl_vader_single_copy_mechanism=none
+ENV PT_HPU_LAZY_ACC_PAR_MODE=0
+ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=1
+
+# Set timezone to UTC and install essential packages
+ENV DEBIAN_FRONTEND="noninteractive" TZ=Etc/UTC
+RUN apt-get update && apt-get install -y \
+    tzdata \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+# Download and install the static build of ffmpeg
+RUN mkdir -p /usr/local/bin/ffmpeg && \
+    cd /usr/local/bin/ffmpeg && \
+    wget https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz && \
+    tar -xf ffmpeg-release-amd64-static.tar.xz && \
+    cp -a ffmpeg-*-static/ffmpeg /usr/bin/ffmpeg && \
+    cp -a ffmpeg-*-static/ffprobe /usr/bin/ffprobe && \
+    rm -rf /usr/local/bin/ffmpeg
+
+COPY . /workspace/whisper
+WORKDIR /workspace/whisper
+
+# Copy HPU requirements
+COPY requirements_hpu.txt /workspace/requirements_hpu.txt
+
+# Install Python packages
+RUN pip install --upgrade pip \
+    && pip install -r requirements_hpu.txt

README.md

@@ -93,6 +93,10 @@ Adding `--task translate` will translate the speech into English:
 
     whisper japanese.wav --language Japanese --task translate
 
+The following command will transcribe speech in audio files, using the Intel® Gaudi® HPU (`--device hpu` option):
+
+    whisper audio.flac audio.mp3 audio.wav --model turbo --device hpu
+
 Run the following to view all available options:
 
     whisper --help
@@ -140,6 +144,61 @@ result = whisper.decode(model, mel, options)
 print(result.text)
 ```
 
+## Intel® Gaudi® hpu usage
+
+### Build the Docker Image
+
+```bash
+docker build -t whisper_hpu:latest -f Dockerfile.hpu .
+```
+
+In the `Dockerfile.hpu`, we use the `vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.3.1:latest` base image, make sure to replace it with the appropriate version for your environment if needed.
+See the [PyTorch Docker Images for the Intel® Gaudi® Accelerator](https://developer.habana.ai/catalog/pytorch-container/) for more information.
+
+### Run the Container
+
+```bash
+docker run -it --runtime=habana whisper_hpu:latest
+```
+
+Using a mapping volume (`-v`) is optional, but it allows you to access the Whisper repository from within the container. 
+You can make this by adding `-v /path/to/your/whisper:/workspace/whisper` to the `docker run` command.
+If you decide to use the mapping make sure to replace `/path/to/your/whisper` with the path to the Whisper repository on your local machine.
+
+### Command-line usage with Intel® Gaudi® hpu
+
+To run the `transcribe` process with Intel® Gaudi® HPU, you can use the `--device hpu` option:
+
+```bash
+python3 -m whisper.transcribe audio_file.wav --model turbo --device hpu
+```
+
+* Note: Change `audio_file.wav` to the path of the audio file you want to transcribe. (Example file: https://www.kaggle.com/datasets/pavanelisetty/sample-audio-files-for-speech-recognition?resource=download)
+
+To run the `transcribe` tests with Intel® Gaudi® HPU, make sure to install the `pytest` package:
+
+```bash
+pip install pytest
+```
+
+and run the following command:
+
+```bash
+PYTHONPATH=. pytest -s tests/test_transcribe.py::test_transcribe_hpu
+```
+
+### Python usage with Intel® Gaudi® hpu
+
+To use Intel® Gaudi® hpu within Python, you can specify the device when loading the model:
+
+```python
+import whisper
+
+model = whisper.load_model("turbo", device="hpu")
+result = model.transcribe("audio.mp3")
+print(result["text"])
+```
+
 ## More examples
 
 Please use the [🙌 Show and tell](https://github.com/openai/whisper/discussions/categories/show-and-tell) category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc.

notebooks/LibriSpeech.ipynb

@@ -36,10 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "3CqtR2Fi5-vP"
-   },
+   "execution_count": null,
    "outputs": [],
    "source": [
     "import os\n",
@@ -56,10 +53,35 @@
     "import torchaudio\n",
     "\n",
     "from tqdm.notebook import tqdm\n",
-    "\n",
-    "\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Set the `DEVICE` based on available hardware\n",
+    "### If you're running on a machine with Intel Gaudi support, you can set the DEVICE to \"hpu\"\n",
+    "### This can be done by setting the device to \"hpu\" if Gaudi is available.\n",
+    "### Ensure you have the Habana PyTorch extension installed and properly configured."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# DEVICE = \"hpu\"\n",
     "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",

notebooks/Multilingual_ASR.ipynb

@@ -51,10 +51,33 @@
     "\n",
     "\n",
     "pd.options.display.max_rows = 100\n",
-        "pd.options.display.max_colwidth = 1000\n",
-        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+    "pd.options.display.max_colwidth = 1000\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Set the `DEVICE` based on available hardware\n",
+    "### If you're running on a machine with Intel Gaudi support, you can set the DEVICE to \"hpu\"\n",
+    "### This can be done by setting the device to \"hpu\" if Gaudi is available.\n",
+    "### Ensure you have the Habana PyTorch extension installed and properly configured."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# DEVICE = \"hpu\"\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -860,8 +883,7 @@
      "text": [
       "Importing the dtw module. When using in academic works please cite:\n",
       "  T. Giorgino. Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package.\n",
-            "  J. Stat. Soft., doi:10.18637/jss.v031.i07.\n",
-            "\n"
+      "  J. Stat. Soft., doi:10.18637/jss.v031.i07.\n"
      ]
     }
    ],

requirements.txt

@@ -5,3 +5,5 @@ tqdm
 more-itertools
 tiktoken
 triton>=2.0.0;platform_machine=="x86_64" and sys_platform=="linux" or sys_platform=="linux2"
+scipy
+pytest

requirements_hpu.txt

@@ -0,0 +1,6 @@
+optimum-habana==1.14.1
+transformers==4.45.2
+huggingface-hub==0.26.2
+tiktoken==0.8.0
+torch-geometric==2.6.1
+numba==0.60.0

tests/conftest.py

@@ -6,6 +6,7 @@ import pytest
 
 def pytest_configure(config):
     config.addinivalue_line("markers", "requires_cuda")
+    config.addinivalue_line("markers", "requires_hpu")
 
 
 @pytest.fixture

tests/test_timing.py

@@ -3,7 +3,8 @@ import pytest
 import scipy.ndimage
 import torch
 
-from whisper.timing import dtw_cpu, dtw_cuda, median_filter
+from whisper.hpu_utils import get_x_hpu
+from whisper.timing import dtw_cpu, dtw_cuda, median_filter, dtw_hpu
 
 sizes = [
     (10, 20),
@@ -94,3 +95,15 @@ def test_median_filter_equivalence(shape):
         filtered_gpu = median_filter(x.cuda(), filter_width).cpu()
 
         assert np.allclose(filtered_cpu, filtered_gpu)
+
+
+@pytest.mark.requires_hpu
+@pytest.mark.parametrize("N, M", sizes)
+def test_dtw_hpu_equivalence(N: int, M: int):
+    x_numpy = np.random.randn(N, M).astype(np.float32)
+    x_hpu = get_x_hpu(x_numpy)
+
+    trace_cpu = dtw_cpu(x_numpy)
+    trace_hpu = dtw_hpu(x_hpu)
+
+    assert np.allclose(trace_cpu, trace_hpu)

tests/test_transcribe.py

@@ -10,7 +10,7 @@ from whisper.tokenizer import get_tokenizer
 @pytest.mark.parametrize("model_name", whisper.available_models())
 def test_transcribe(model_name: str):
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    model = whisper.load_model(model_name).to(device)
+    model = whisper.load_model(model_name, device=device)
     audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
 
     language = "en" if model_name.endswith(".en") else None
@@ -40,3 +40,38 @@ def test_transcribe(model_name: str):
                 timing_checked = True
 
     assert timing_checked
+
+
+@pytest.mark.requires_hpu
+@pytest.mark.parametrize("model_name", whisper.available_models())
+def test_transcribe_hpu(model_name: str):
+    device = "hpu"
+    model = whisper.load_model(model_name, device=device)
+    audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
+
+    language = "en" if model_name.endswith(".en") else None
+    result = model.transcribe(
+        audio_path, language=language, temperature=0.0, word_timestamps=True
+    )
+    assert result["language"] == "en"
+    assert result["text"] == "".join([s["text"] for s in result["segments"]])
+
+    transcription = result["text"].lower()
+    assert "my fellow americans" in transcription
+    assert "your country" in transcription
+    assert "do for you" in transcription
+
+    tokenizer = get_tokenizer(model.is_multilingual, num_languages=model.num_languages)
+    all_tokens = [t for s in result["segments"] for t in s["tokens"]]
+    assert tokenizer.decode(all_tokens) == result["text"]
+
+    timing_checked = False
+    for segment in result["segments"]:
+        for timing in segment["words"]:
+            assert timing["start"] < timing["end"]
+            if timing["word"].strip(" ,") == "Americans":
+                assert timing["start"] <= 1.8
+                assert timing["end"] >= 1.8
+                timing_checked = True
+
+    assert timing_checked

whisper/__init__.py

@@ -147,14 +147,31 @@ def load_model(
     with (
         io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
     ) as fp:
+        if device == "hpu":
+            """If the device is HPU,
+            the model should be loaded on CPU first
+            and then moved to HPU."""
+            checkpoint = torch.load(fp, map_location="cpu")
+        else:
             checkpoint = torch.load(fp, map_location=device)
     del checkpoint_file
 
     dims = ModelDimensions(**checkpoint["dims"])
-    model = Whisper(dims)
+    model = Whisper(dims, compute_device=torch.device(device))
     model.load_state_dict(checkpoint["model_state_dict"])
 
     if alignment_heads is not None:
         model.set_alignment_heads(alignment_heads)
 
+    if device == "hpu":
+        from habana_frameworks.torch.utils.library_loader import load_habana_module
+
+        load_habana_module()
+        if torch.hpu.is_available():
+            from habana_frameworks.torch.hpu import wrap_in_hpu_graph
+
+            model = wrap_in_hpu_graph(model)
+            model = model.eval().to(torch.device(device))
+
+            return model
     return model.to(device)

whisper/decoding.py

@@ -8,6 +8,7 @@ from torch import Tensor
 from torch.distributions import Categorical
 
 from .audio import CHUNK_LENGTH
+from .hpu_utils import is_hpu_device
 from .tokenizer import Tokenizer, get_tokenizer
 from .utils import compression_ratio
 
@@ -456,6 +457,16 @@ class ApplyTimestampRules(LogitFilter):
 
         # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
         for k in range(tokens.shape[0]):
+            if is_hpu_device(tokens.device):
+                """
+                If tokens are on HPU, `sampled_tokens` is cloned to force evaluation.
+
+                On Habana HPUs, tensors may use lazy execution, which can lead to runtime errors if not explicitly 
+                evaluated. Cloning `sampled_tokens` ensures it is fully evaluated on the HPU, preventing potential 
+                synchronization issues.
+                """
+                sampled_tokens = tokens[k, self.sample_begin :].clone()
+            else:
                 sampled_tokens = tokens[k, self.sample_begin :]
             seq = [t for t in sampled_tokens.tolist()]
             last_was_timestamp = (

whisper/hpu_model_tests.py

@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes=10):
+        super(SimpleCNN, self).__init__()
+
+        # Define layers
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        self.fc1 = nn.Linear(32 * 56 * 56, 128)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        # Forward pass through the network
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 32 * 56 * 56)  # Flatten the tensor
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+# Example usage
+if __name__ == "__main__":
+    # Load Habana module for HPU support
+    from habana_frameworks.torch.utils.library_loader import load_habana_module
+    import habana_frameworks.torch.hpu as hthpu
+
+    load_habana_module()
+
+    device = None
+    # Set device to HPU
+    if hthpu.is_available():
+        device = torch.device("hpu")
+        print("Using HPU")
+
+    if not device:
+        print("HPU is not available")
+        exit(1)
+
+    # Create model instance and move it to the HPU
+    model = SimpleCNN(num_classes=10).to(device)
+
+    # Create a dummy input tensor and move it to the HPU
+    input_tensor = torch.rand((64, 3, 224, 224), device=device)  # Batch size of 64
+
+    # Forward pass through the model on HPU
+    output = model(input_tensor)
+
+    print("Output shape:", output.shape)  # Should be [64, num_classes]

whisper/hpu_utils.py

@@ -0,0 +1,13 @@
+import torch
+
+def get_x_hpu(x_numpy):
+    from habana_frameworks.torch.utils.library_loader import load_habana_module
+
+    load_habana_module()
+
+    x_hpu = torch.from_numpy(x_numpy).to("hpu")
+    return x_hpu
+
+
+def is_hpu_device(device: torch.device):
+    return device in (torch.device("hpu:0"), torch.device("hpu"))

whisper/model.py

@@ -11,6 +11,7 @@ from torch import Tensor, nn
 
 from .decoding import decode as decode_function
 from .decoding import detect_language as detect_language_function
+from .hpu_utils import is_hpu_device
 from .transcribe import transcribe as transcribe_function
 
 try:
@@ -250,9 +251,12 @@ class TextDecoder(nn.Module):
 
 
 class Whisper(nn.Module):
-    def __init__(self, dims: ModelDimensions):
+    def __init__(self, dims: ModelDimensions, compute_device: Optional[torch.device] = None):
         super().__init__()
         self.dims = dims
+        self.compute_device = compute_device or (
+            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+        )
         self.encoder = AudioEncoder(
             self.dims.n_mels,
             self.dims.n_audio_ctx,
@@ -273,7 +277,11 @@ class Whisper(nn.Module):
             self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
         )
         all_heads[self.dims.n_text_layer // 2 :] = True
-        self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)
+        if not is_hpu_device(self.compute_device):
+            # Convert to sparse format if device is not HPU
+            all_heads = all_heads.to_sparse()
+
+        self.register_buffer("alignment_heads", all_heads, persistent=False)
 
     def set_alignment_heads(self, dump: bytes):
         array = np.frombuffer(
@@ -282,7 +290,11 @@ class Whisper(nn.Module):
         mask = torch.from_numpy(array).reshape(
             self.dims.n_text_layer, self.dims.n_text_head
         )
-        self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)
+        if not is_hpu_device(self.compute_device):
+            # Convert to sparse format if device is not HPU
+            mask = mask.to_sparse()
+
+        self.register_buffer("alignment_heads", mask, persistent=False)
 
     def embed_audio(self, mel: torch.Tensor):
         return self.encoder(mel)

whisper/timing.py

@@ -10,6 +10,7 @@ import torch
 import torch.nn.functional as F
 
 from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND
+from .hpu_utils import is_hpu_device
 from .tokenizer import Tokenizer
 
 if TYPE_CHECKING:
@@ -138,7 +139,54 @@ def dtw_cuda(x, BLOCK_SIZE=1024):
     return backtrace(trace.cpu().numpy())
 
 
+def dtw_hpu(x, BLOCK_SIZE=1024):
+    """
+    DTW implementation for HPU.
+    """
+    M, N = x.shape
+    assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}"
+
+    x_skew = F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M)
+    x_skew = x_skew.T.contiguous()
+
+    # Initialize cost and trace matrices with high values for comparison
+    cost = torch.ones(N + M + 2, M + 2, device="hpu") * np.inf
+    cost[0, 0] = 0  # Start point for DTW
+    trace = torch.zeros_like(cost, dtype=torch.int32, device="hpu")
+
+    for k in range(1, N + M + 1):
+        p0 = cost[k - 1, :M]
+        p1 = cost[k, :M]
+        p2 = cost[k, 1:M + 1]
+
+        c0 = p0.clone()
+        c1 = p1.clone()
+        c2 = p2.clone()
+
+        x_row = x_skew[k - 1, :M]
+
+        cost_row = x_row + torch.min(torch.min(c0, c1), c2)
+        cost[k + 1, 1:M + 1] = cost_row
+
+        # Track path by storing traces
+        trace[k + 1, 1:M + 1] = 2 * (c2 <= c0) * (c2 <= c1) + 1 * (c1 <= c0) * (c1 <= c2) + 0 * (c0 <= c1) * (c0 <= c2)
+
+    trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[:, : N + 1]
+    return backtrace(trace.cpu().numpy())
+
+
 def dtw(x: torch.Tensor) -> np.ndarray:
+    try:
+        from habana_frameworks.torch.utils.library_loader import load_habana_module
+        load_habana_module()
+
+        if torch.hpu.is_available():
+            return dtw_hpu(x)
+    except (ImportError, subprocess.CalledProcessError):
+        warnings.warn(
+            "Failed to import Habana modules, likely due to missing Habana libraries; "
+        )
+
     if x.is_cuda:
         try:
             return dtw_cuda(x)
@@ -204,7 +252,21 @@ def find_alignment(
         hook.remove()
 
     # heads * tokens * frames
-    weights = torch.stack([QKs[_l][_h] for _l, _h in model.alignment_heads.indices().T])
+    # Adjust alignment head indices for HPU
+    weights = []
+    if is_hpu_device(model.device):
+        # Handle dense layout for HPU
+        alignment_heads_dense = model.alignment_heads.to_dense() if model.alignment_heads.is_sparse else model.alignment_heads
+        indices = alignment_heads_dense.nonzero(as_tuple=True)
+        for _l, _h in zip(*indices):
+            weights.append(QKs[_l][_h])
+    else:
+        # Default behavior for non-HPU devices
+        for _l, _h in model.alignment_heads.indices().T:
+            weights.append(QKs[_l][_h])
+
+    # Stack the weights
+    weights = torch.stack(weights)
     weights = weights[:, :, : num_frames // 2]
     weights = (weights * qk_scale).softmax(dim=-1)
     std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False)

whisper/transcribe.py

@@ -18,6 +18,7 @@ from .audio import (
     pad_or_trim,
 )
 from .decoding import DecodingOptions, DecodingResult
+from .hpu_utils import is_hpu_device
 from .timing import add_word_timestamps
 from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
 from .utils import (
@@ -126,6 +127,11 @@ def transcribe(
             warnings.warn("FP16 is not supported on CPU; using FP32 instead")
             dtype = torch.float32
 
+    if is_hpu_device(model.device):
+        if dtype == torch.float16:
+            warnings.warn("FP16 is not supported on HPU; using FP32 instead")
+            dtype = torch.float32
+
     if dtype == torch.float32:
         decode_options["fp16"] = False
 
@@ -508,12 +514,26 @@ def cli():
             f"model should be one of {available_models()} or path to a model checkpoint"
         )
 
+    def valid_device(device_name):
+        if device_name == "cuda" and not torch.cuda.is_available():
+            warnings.warn("CUDA is not available; using CPU instead")
+            device_name = "cpu"
+        if device_name == "hpu":
+            from habana_frameworks.torch.utils.library_loader import load_habana_module
+
+            load_habana_module()
+            if not torch.hpu.is_available():
+                warnings.warn("HPU is not available; using CPU instead")
+            device_name = "hpu"
+
+        return device_name
+
     # fmt: off
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
     parser.add_argument("--model", default="turbo", type=valid_model_name, help="name of the Whisper model to use")
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
-    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", type=valid_device, help="device to use for PyTorch inference (hpu/cuda/cpu)")
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
     parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced")
     parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")