chore(client): Support Pydantic 2 (#900)

This should allow users to use either Pydantic 2 or Pydantic 1.

I couldn't run all tests locally because I reran them too often and got
rate limited, but I believe this is sufficient.

clients/python/pyproject.toml

@@ -12,7 +12,7 @@ repository = "https://github.com/huggingface/text-generation-inference"
 
 [tool.poetry.dependencies]
 python = "^3.7"
-pydantic = "^1.10"
+pydantic = "> 1.10, < 3"
 aiohttp = "^3.8"
 huggingface-hub = ">= 0.12, < 1.0"
 

clients/python/text_generation/types.py

@@ -18,21 +18,21 @@ class Parameters(BaseModel):
     # Stop generating tokens if a member of `stop_sequences` is generated
     stop: List[str] = []
     # Random sampling seed
-    seed: Optional[int]
+    seed: Optional[int] = None
     # The value used to module the logits distribution.
-    temperature: Optional[float]
+    temperature: Optional[float] = None
     # The number of highest probability vocabulary tokens to keep for top-k-filtering.
-    top_k: Optional[int]
+    top_k: Optional[int] = None
     # 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.
-    top_p: Optional[float]
+    top_p: Optional[float] = None
     # truncate inputs tokens to the given size
-    truncate: Optional[int]
+    truncate: Optional[int] = None
     # Typical Decoding mass
     # See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
-    typical_p: Optional[float]
+    typical_p: Optional[float] = None
     # Generate best_of sequences and return the one if the highest token logprobs
-    best_of: Optional[int]
+    best_of: Optional[int] = None
     # Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
     watermark: bool = False
     # Get generation details
@@ -114,7 +114,7 @@ class Request(BaseModel):
     # Prompt
     inputs: str
     # Generation parameters
-    parameters: Optional[Parameters]
+    parameters: Optional[Parameters] = None
     # Whether to stream output tokens
     stream: bool = False
 
@@ -145,7 +145,7 @@ class InputToken(BaseModel):
     text: str
     # Logprob
     # Optional since the logprob of the first token cannot be computed
-    logprob: Optional[float]
+    logprob: Optional[float] = None
 
 
 # Generated tokens
@@ -180,7 +180,7 @@ class BestOfSequence(BaseModel):
     # Number of generated tokens
     generated_tokens: int
     # Sampling seed if sampling was activated
-    seed: Optional[int]
+    seed: Optional[int] = None
     # Decoder input tokens, empty if decoder_input_details is False
     prefill: List[InputToken]
     # Generated tokens
@@ -196,7 +196,7 @@ class Details(BaseModel):
     # Number of generated tokens
     generated_tokens: int
     # Sampling seed if sampling was activated
-    seed: Optional[int]
+    seed: Optional[int] = None
     # Decoder input tokens, empty if decoder_input_details is False
     prefill: List[InputToken]
     # Generated tokens
@@ -204,7 +204,7 @@ class Details(BaseModel):
     # Most likely tokens
     top_tokens: Optional[List[List[Token]]]
     # Additional sequences when using the `best_of` parameter
-    best_of_sequences: Optional[List[BestOfSequence]]
+    best_of_sequences: Optional[List[BestOfSequence]] = None
 
 
 # `generate` return value
@@ -222,7 +222,7 @@ class StreamDetails(BaseModel):
     # Number of generated tokens
     generated_tokens: int
     # Sampling seed if sampling was activated
-    seed: Optional[int]
+    seed: Optional[int] = None
 
 
 # `generate_stream` return value
@@ -233,10 +233,10 @@ class StreamResponse(BaseModel):
     top_tokens: Optional[List[Token]]
     # Complete generated text
     # Only available when the generation is finished
-    generated_text: Optional[str]
+    generated_text: Optional[str] = None
     # Generation details
     # Only available when the generation is finished
-    details: Optional[StreamDetails]
+    details: Optional[StreamDetails] = None
 
 
 # Inference API currently deployed model