feat(launcher): allow parsing num_shard from CUDA_VISIBLE_DEVICES (#107)

clients/python/README.md

@@ -1,7 +1,8 @@
 # Text Generation
 
 The Hugging Face Text Generation Python library provides a convenient way of interfacing with a
-`text-generation-inference` instance running on your own infrastructure or on the Hugging Face Hub.
+`text-generation-inference` instance running on
+[Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints) or on the Hugging Face Hub.
 
 ## Get Started
 
@@ -11,7 +12,7 @@ The Hugging Face Text Generation Python library provides a convenient way of int
 pip install text-generation
 ```
 
-### Usage
+### Inference API Usage
 
 ```python
 from text_generation import InferenceAPIClient
@@ -50,3 +51,128 @@ async for response in client.generate_stream("Why is the sky blue?"):
 print(text)
 # ' Rayleigh scattering'
 ```
+
+### Hugging Fae Inference Endpoint usage
+
+```python
+from text_generation import Client
+
+endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
+
+client = Client(endpoint_url)
+text = client.generate("Why is the sky blue?").generated_text
+print(text)
+# ' Rayleigh scattering'
+
+# Token Streaming
+text = ""
+for response in client.generate_stream("Why is the sky blue?"):
+    if not response.token.special:
+        text += response.token.text
+
+print(text)
+# ' Rayleigh scattering'
+```
+
+or with the asynchronous client:
+
+```python
+from text_generation import AsyncClient
+
+endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud"
+
+client = AsyncClient(endpoint_url)
+response = await client.generate("Why is the sky blue?")
+print(response.generated_text)
+# ' Rayleigh scattering'
+
+# Token Streaming
+text = ""
+async for response in client.generate_stream("Why is the sky blue?"):
+    if not response.token.special:
+        text += response.token.text
+
+print(text)
+# ' Rayleigh scattering'
+```
+
+### Types
+
+```python
+# Prompt tokens
+class PrefillToken:
+    # Token ID from the model tokenizer
+    id: int
+    # Token text
+    text: str
+    # Logprob
+    # Optional since the logprob of the first token cannot be computed
+    logprob: Optional[float]
+
+
+# Generated tokens
+class Token:
+    # Token ID from the model tokenizer
+    id: int
+    # Token text
+    text: str
+    # Logprob
+    logprob: float
+    # Is the token a special token
+    # Can be used to ignore tokens when concatenating
+    special: bool
+
+
+# Generation finish reason
+class FinishReason(Enum):
+    # number of generated tokens == `max_new_tokens`
+    Length = "length"
+    # the model generated its end of sequence token
+    EndOfSequenceToken = "eos_token"
+    # the model generated a text included in `stop_sequences`
+    StopSequence = "stop_sequence"
+
+
+# `generate` details
+class Details:
+    # Generation finish reason
+    finish_reason: FinishReason
+    # Number of generated tokens
+    generated_tokens: int
+    # Sampling seed if sampling was activated
+    seed: Optional[int]
+    # Prompt tokens
+    prefill: List[PrefillToken]
+    # Generated tokens
+    tokens: List[Token]
+
+
+# `generate` return value
+class Response:
+    # Generated text
+    generated_text: str
+    # Generation details
+    details: Details
+
+
+# `generate_stream` details
+class StreamDetails:
+    # Generation finish reason
+    finish_reason: FinishReason
+    # Number of generated tokens
+    generated_tokens: int
+    # Sampling seed if sampling was activated
+    seed: Optional[int]
+
+
+# `generate_stream` return value
+class StreamResponse:
+    # Generated token
+    token: Token
+    # Complete generated text
+    # Only available when the generation is finished
+    generated_text: Optional[str]
+    # Generation details
+    # Only available when the generation is finished
+    details: Optional[StreamDetails]
+```

clients/python/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "text-generation"
-version = "0.1.0"
+version = "0.2.0"
 description = "Hugging Face Text Generation Python Client"
 license = "Apache-2.0"
 authors = ["Olivier Dehaene <olivier@huggingface.co>"]

clients/python/text_generation/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.3.2"
+__version__ = "0.2.0"
 
 from text_generation.client import Client, AsyncClient
 from text_generation.inference_api import InferenceAPIClient, InferenceAPIAsyncClient

clients/python/text_generation/client.py

@@ -63,7 +63,7 @@ class Client:
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
-        watermarking: bool = False,
+        watermark: bool = False,
     ) -> Response:
         """
         Given a prompt, generate the following text
@@ -91,7 +91,7 @@ class Client:
             top_p (`float`):
                 If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
                 higher are kept for generation.
-            watermarking (`bool`):
+            watermark (`bool`):
                 Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 
         Returns:
@@ -109,7 +109,7 @@ class Client:
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
-            watermark=watermarking,
+            watermark=watermark,
         )
         request = Request(inputs=prompt, stream=False, parameters=parameters)
 
@@ -136,7 +136,7 @@ class Client:
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
-        watermarking: bool = False,
+        watermark: bool = False,
     ) -> Iterator[StreamResponse]:
         """
         Given a prompt, generate the following stream of tokens
@@ -164,7 +164,7 @@ class Client:
             top_p (`float`):
                 If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
                 higher are kept for generation.
-            watermarking (`bool`):
+            watermark (`bool`):
                 Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 
         Returns:
@@ -182,7 +182,7 @@ class Client:
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
-            watermark=watermarking,
+            watermark=watermark,
         )
         request = Request(inputs=prompt, stream=True, parameters=parameters)
 
@@ -268,7 +268,7 @@ class AsyncClient:
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
-        watermarking: bool = False,
+        watermark: bool = False,
     ) -> Response:
         """
         Given a prompt, generate the following text asynchronously
@@ -296,7 +296,7 @@ class AsyncClient:
             top_p (`float`):
                 If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
                 higher are kept for generation.
-            watermarking (`bool`):
+            watermark (`bool`):
                 Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 
         Returns:
@@ -314,7 +314,7 @@ class AsyncClient:
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
-            watermark=watermarking,
+            watermark=watermark,
         )
         request = Request(inputs=prompt, stream=False, parameters=parameters)
 
@@ -338,7 +338,7 @@ class AsyncClient:
         temperature: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
-        watermarking: bool = False,
+        watermark: bool = False,
     ) -> AsyncIterator[StreamResponse]:
         """
         Given a prompt, generate the following stream of tokens asynchronously
@@ -366,7 +366,7 @@ class AsyncClient:
             top_p (`float`):
                 If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
                 higher are kept for generation.
-            watermarking (`bool`):
+            watermark (`bool`):
                 Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 
         Returns:
@@ -384,7 +384,7 @@ class AsyncClient:
             temperature=temperature,
             top_k=top_k,
             top_p=top_p,
-            watermark=watermarking,
+            watermark=watermark,
         )
         request = Request(inputs=prompt, stream=True, parameters=parameters)
 

launcher/src/main.rs

@@ -23,8 +23,10 @@ struct Args {
     model_id: String,
     #[clap(long, env)]
     revision: Option<String>,
-    #[clap(default_value = "1", long, env)]
+    #[clap(long, env)]
-    num_shard: usize,
+    sharded: Option<bool>,
+    #[clap(long, env)]
+    num_shard: Option<usize>,
     #[clap(long, env)]
     quantize: bool,
     #[clap(default_value = "128", long, env)]
@@ -80,6 +82,7 @@ fn main() -> ExitCode {
     let Args {
         model_id,
         revision,
+        sharded,
         num_shard,
         quantize,
         max_concurrent_requests,
@@ -102,6 +105,49 @@ fn main() -> ExitCode {
         watermark_delta,
     } = args;
 
+    // get the number of shards given `sharded` and `num_shard`
+    let num_shard = if let Some(sharded) = sharded {
+        // sharded is set
+        match sharded {
+            // sharded is set and true
+            true => {
+                match num_shard {
+                    None => {
+                        // try to default to the number of available GPUs
+                        tracing::info!("Parsing num_shard from CUDA_VISIBLE_DEVICES");
+                        let cuda_visible_devices = env::var("CUDA_VISIBLE_DEVICES")
+                            .expect("--num-shard and CUDA_VISIBLE_DEVICES are not set");
+                        let n_devices = cuda_visible_devices.split(",").count();
+                        if n_devices <= 1 {
+                            panic!("`sharded` is true but only found {n_devices} CUDA devices");
+                        }
+                        tracing::info!("Sharding on {n_devices} found CUDA devices");
+                        n_devices
+                    }
+                    Some(num_shard) => {
+                        // we can't have only one shard while sharded
+                        if num_shard <= 1 {
+                            panic!("`sharded` is true but `num_shard` <= 1");
+                        }
+                        num_shard
+                    }
+                }
+            }
+            // sharded is set and false
+            false => {
+                let num_shard = num_shard.unwrap_or(1);
+                // we can't have more than one shard while not sharded
+                if num_shard != 1 {
+                    panic!("`sharded` is false but `num_shard` != 1");
+                }
+                num_shard
+            }
+        }
+    } else {
+        // default to a single shard
+        num_shard.unwrap_or(1)
+    };
+
     // Signal handler
     let running = Arc::new(AtomicBool::new(true));
     let r = running.clone();