feat: Support sampling seeding (#37)

Co-authored-by: Yannic Kilcher <yk@users.noreply.github.com>

proto/generate.proto

@@ -36,6 +36,8 @@ message NextTokenChooserParameters {
     float top_p = 3;
     /// apply sampling on the logits
     bool do_sample = 4;
+    /// random seed for sampling
+    optional uint64 seed = 5;
 }
 
 message StoppingCriteriaParameters {
@@ -82,6 +84,8 @@ message GeneratedText {
     repeated float logprobs = 6;
     /// Finish reason
     string finish_reason = 7;
+    /// Seed
+    optional uint64 seed = 8;
 }
 
 message GenerateRequest {

router/client/build.rs

 use std::fs;
 
 fn main() -> Result<(), Box<dyn std::error::Error>> {
+    println!("cargo:rerun-if-changed=../../proto/generate.proto");
     fs::create_dir("src/pb").unwrap_or(());
     tonic_build::configure()
         .build_client(true)

router/src/batcher.rs

@@ -191,6 +191,7 @@ fn send_generated(finished: Vec<GeneratedText>, entries: &mut IntMap<u64, Entry>
             tokens: output.tokens,
             logprobs: output.logprobs,
             finish_reason: output.finish_reason,
+            seed: output.seed,
             queued: entry.time,
             start: entry.batch_time.unwrap(), // unwrap is always valid
             end: Instant::now(),
@@ -208,6 +209,7 @@ pub(crate) struct InferResponse {
     pub(crate) tokens: Vec<String>,
     pub(crate) logprobs: Vec<f32>,
     pub(crate) finish_reason: String,
+    pub(crate) seed: Option<u64>,
     pub(crate) queued: Instant,
     pub(crate) start: Instant,
     pub(crate) end: Instant,

router/src/db.rs

@@ -166,6 +166,7 @@ impl From<&GenerateParameters> for NextTokenChooserParameters {
             top_k: parameters.top_k as u32,
             top_p: parameters.top_p,
             do_sample: parameters.do_sample,
+            seed: parameters.seed,
         }
     }
 }

router/src/lib.rs

@@ -25,6 +25,8 @@ pub(crate) struct GenerateParameters {
     pub stop: Vec<String>,
     #[serde(default)]
     pub details: bool,
+    #[serde(default)]
+    pub seed: Option<u64>,
 }
 
 fn default_temperature() -> f32 {
@@ -56,6 +58,7 @@ fn default_parameters() -> GenerateParameters {
         max_new_tokens: default_max_new_tokens(),
         stop: vec![],
         details: false,
+        seed: None,
     }
 }
 
@@ -70,6 +73,7 @@ pub(crate) struct GenerateRequest {
 pub(crate) struct Details {
     pub finish_reason: String,
     pub generated_tokens: u32,
+    pub seed: Option<u64>,
     pub tokens: Vec<(u32, String, f32)>,
 }
 

router/src/server.rs

@@ -55,6 +55,7 @@ async fn health(state: Extension<ServerState>) -> Result<(), (StatusCode, Json<E
                     max_new_tokens: 1,
                     stop: vec![],
                     details: false,
+                    seed: None,
                 },
             },
         )
@@ -70,7 +71,8 @@ async fn health(state: Extension<ServerState>) -> Result<(), (StatusCode, Json<E
         validation_time,
         queue_time,
         inference_time,
-        time_per_token
+        time_per_token,
+        seed
     )
 )]
 async fn generate(
@@ -118,6 +120,7 @@ async fn generate(
                 .map(|((id, text), logprob)| (id, text, logprob))
                 .collect();
             Some(Details {
+                seed: response.seed,
                 finish_reason: response.finish_reason,
                 generated_tokens: response.generated_tokens,
                 tokens,
@@ -162,6 +165,7 @@ async fn generate(
     tracing::Span::current().record("queue_time", format!("{:?}", queue_time));
     tracing::Span::current().record("inference_time", format!("{:?}", inference_time));
     tracing::Span::current().record("time_per_token", format!("{:?}", time_per_token));
+    tracing::Span::current().record("seed", format!("{:?}", response.seed));
     tracing::info!("Output: {}", response.output_text);
 
     // Send response

server/text_generation/models/bloom.py

@@ -234,7 +234,9 @@ class BLOOMSharded(BLOOM):
                     if name == "word_embeddings.weight":
                         model.lm_head._parameters["weight"] = tensor
 
-    def forward(self, input_ids, attention_mask, position_ids, past_key_values: Optional = None):
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
         outputs = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,

server/text_generation/models/causal_lm.py

@@ -7,7 +7,7 @@ from typing import Optional, Tuple, List, Type
 from text_generation.models import Model
 from text_generation.models.types import GeneratedText, Batch
 from text_generation.pb import generate_pb2
-from text_generation.utils import NextTokenChooser, StoppingCriteria
+from text_generation.utils import NextTokenChooser, StoppingCriteria, Sampling
 
 
 @dataclass
@@ -296,7 +296,10 @@ class CausalLM(Model):
         )
         with context_manager():
             logits, past = self.forward(
-                batch.input_ids, batch.attention_mask, batch.position_ids, batch.past_key_values
+                batch.input_ids,
+                batch.attention_mask,
+                batch.position_ids,
+                batch.past_key_values,
             )
 
         # List of indices to cache
@@ -373,6 +376,12 @@ class CausalLM(Model):
                     1
                 ).tolist()
 
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
                 # Add to the list of finished generations with the original request
                 generated_texts.append(
                     GeneratedText(
@@ -383,6 +392,7 @@ class CausalLM(Model):
                         token_ids=token_ids.squeeze(1).tolist(),
                         logprobs=logprobs,
                         reason=reason,
+                        seed=seed,
                     )
                 )
             # add to the next batch

server/text_generation/models/galactica.py

@@ -333,7 +333,9 @@ class GalacticaSharded(Galactica):
                     if name == "model.decoder.embed_tokens.weight":
                         model.lm_head._parameters["weight"] = tensor
 
-    def forward(self, input_ids, attention_mask, position_ids, past_key_values: Optional = None):
+    def forward(
+        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
+    ):
         outputs = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,

server/text_generation/models/santacoder.py

@@ -39,12 +39,16 @@ class SantaCoder(CausalLM):
             }
         )
 
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=dtype,
-            load_in_8bit=quantize,
-            trust_remote_code=True,  # required
-        ).to(device).eval()
+        self.model = (
+            AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=dtype,
+                load_in_8bit=quantize,
+                trust_remote_code=True,  # required
+            )
+            .to(device)
+            .eval()
+        )
 
         super(CausalLM, self).__init__(
             tokenizer=tokenizer,

server/text_generation/models/seq2seq_lm.py

@@ -7,7 +7,7 @@ from typing import Optional, Tuple, List, Type
 from text_generation.models import Model
 from text_generation.models.types import GeneratedText, Batch
 from text_generation.pb import generate_pb2
-from text_generation.utils import NextTokenChooser, StoppingCriteria
+from text_generation.utils import NextTokenChooser, StoppingCriteria, Sampling
 
 
 @dataclass
@@ -451,6 +451,13 @@ class Seq2SeqLM(Model):
                 logprobs = [float("nan")] + decoder_logprobs[
                     -decoder_input_length:
                 ].tolist()
+
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
                 # Add to the list of finished generations with the original request
                 generated_texts.append(
                     GeneratedText(
@@ -461,6 +468,7 @@ class Seq2SeqLM(Model):
                         token_ids=token_ids.tolist(),
                         logprobs=logprobs,
                         reason=reason,
+                        seed=seed,
                     )
                 )
             # add to the next batch

server/text_generation/models/types.py

@@ -2,7 +2,7 @@ import torch
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import List
+from typing import List, Optional
 
 from transformers import PreTrainedTokenizerBase
 
@@ -39,6 +39,7 @@ class GeneratedText:
     token_ids: List[int]
     logprobs: List[float]
     reason: str
+    seed: Optional[int]
 
     def to_pb(self) -> generate_pb2.GeneratedText:
         return generate_pb2.GeneratedText(
@@ -49,4 +50,5 @@ class GeneratedText:
             token_ids=self.token_ids,
             logprobs=self.logprobs,
             finish_reason=self.reason,
+            seed=self.seed,
         )

server/text_generation/utils.py

@@ -24,11 +24,24 @@ from text_generation.pb import generate_pb2
 
 
 class Sampling:
+    def __init__(self, seed: Optional[int] = None):
+        self.generator = torch.Generator()
+        if seed is not None:
+            self.generator.manual_seed(seed)
+        else:
+            self.generator.seed()
+
     def __call__(self, logits):
         probs = torch.nn.functional.softmax(logits, dim=-1)
-        next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+        next_tokens = torch.multinomial(
+            probs, num_samples=1, generator=self.generator
+        ).squeeze(1)
         return next_tokens
 
+    @property
+    def seed(self) -> int:
+        return self.generator.initial_seed()
+
 
 class Greedy:
     def __call__(self, logits):
@@ -36,7 +49,9 @@ class Greedy:
 
 
 class NextTokenChooser:
-    def __init__(self, temperature=1.0, top_k=None, top_p=None, do_sample=False):
+    def __init__(
+        self, temperature=1.0, top_k=None, top_p=None, do_sample=False, seed=None
+    ):
         warpers = LogitsProcessorList()
         # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
         # all samplers can be found in `generation_utils_samplers.py`
@@ -53,7 +68,7 @@ class NextTokenChooser:
             sampling = True
 
         self.warpers = warpers
-        self.choice = Sampling() if sampling else Greedy()
+        self.choice = Sampling(seed) if sampling else Greedy()
 
     def __call__(self, input_ids, scores):
         # Warp logits
@@ -66,11 +81,14 @@ class NextTokenChooser:
 
     @classmethod
     def from_pb(cls, pb: generate_pb2.NextTokenChooserParameters) -> "NextTokenChooser":
+        # handle protobuf making default values 0
+        seed = pb.seed if pb.HasField("seed") else None
         return NextTokenChooser(
             temperature=pb.temperature,
             top_k=pb.top_k,
             top_p=pb.top_p,
             do_sample=pb.do_sample,
+            seed=seed,
         )