prefix sampling

tests

token length fix

sample prefix tokens not chars

lint

fix tests

percentage

lint fixes

gemini-feedback

fix tests failing

rename prompt-prefix-ratio

mkae current_prefix_length local

remove prompt prefix length

refactor prefix sampling logic

format

revert back to 4 digits

fix prefix length to change with variable distribution

use line char ratio

put latest changes in their own function and update prefix truncation

gemini-feedback

make number of tokens more accurate to scenario

use tokenizer.encode

fix tests

documentation

fix merge issues

Author: TJ5

docs/user-guide/run-benchmark.md

@@ -186,6 +186,11 @@ For heavier traffic scenarios, like `D(16000,200)` or `D(128000,200)`, use the f
             --num-concurrency 32 \
 ```
 
+To benchmark with prefix caching, you can make a given fraction of each prompt a common prefix with `--prompt-prefix-ratio`. For example, to set the first half of each prompt to a common prefix, use:
+
+```shell
+            --prompt-prefix-ratio 0.5 \
+```
 
 ## Distributed Benchmark
 

genai_bench/cli/cli.py

@@ -121,6 +121,7 @@ def benchmark(
     spawn_rate,
     upload_results,
     namespace,
+    prompt_prefix_ratio,
     # Storage auth options
     storage_provider,
     storage_bucket,
@@ -285,6 +286,7 @@ def benchmark(
         data=data,
         additional_request_params=additional_request_params,
         dataset_config=dataset_config_obj,
+        prefix_length=prompt_prefix_ratio,
     )
 
     # If user did not provide scenarios but provided a dataset, default to dataset mode

genai_bench/cli/option_groups.py

@@ -368,6 +368,14 @@ def server_options(func):
 
 # Group experiment-related options
 def experiment_options(func):
+    func = click.option(
+        "--prompt-prefix-ratio",
+        type=click.FloatRange(0.0, 1.0),
+        default=0.0,
+        help="The ratio of prefix length to overall input length "
+        "to prepend to all inputs to test prefix caching. "
+        "Value should be between 0.0 and 1.0. ",
+    )(func)
     func = click.option(
         "--experiment-folder-name",
         type=str,

genai_bench/sampling/text.py

@@ -31,6 +31,7 @@ def __init__(
         model: str,
         output_modality: str,
         data: List[str],
+        prompt_prefix_ratio: float = 0.0,
         additional_request_params: Optional[Dict[str, Any]] = None,
         dataset_config: Optional[DatasetConfig] = None,
         **kwargs,
@@ -41,6 +42,8 @@ def __init__(
 
         self.data = data
         self.batch_size = 1  # Default batch size
+        self.prompt_prefix_ratio = prompt_prefix_ratio
+        self.prefix = ""
 
     def sample(self, scenario: Optional[Scenario]) -> UserRequest:
         """
@@ -167,6 +170,68 @@ def _validate_scenario(self, scenario: Optional[Scenario]) -> None:
                 f"{type(scenario.scenario_type)}"
             )
 
+    def _sample_prefix(self, current_prefix_length) -> str:
+        """
+        Generates prefix of length current_prefix_length to be
+        prepended to all input prompts.
+        """
+
+        data_copy = self.data.copy()
+
+        if not self.data:
+            raise ValueError("Cannot generate prefix from an empty dataset")
+
+        prefix = ""
+        prefix_tokens_len = 0
+        # Generate the prefix
+        while prefix_tokens_len < current_prefix_length:
+            random.shuffle(data_copy)
+            for line in data_copy:
+                line_tokens = self.tokenizer.encode(line)
+                num_line_tokens = len(line_tokens)
+                if prefix_tokens_len + num_line_tokens > current_prefix_length:
+                    remaining_prefix_len = current_prefix_length - prefix_tokens_len
+                    truncated_text = self.tokenizer.decode(
+                        line_tokens[:remaining_prefix_len]
+                    )
+                    prefix += truncated_text
+                    return prefix
+                prefix += line
+                prefix_tokens_len = len(self.tokenizer.encode(prefix))
+
+        return prefix
+
+    def _get_current_prefix(self, prefix_length: int) -> str:
+        """
+        Returns the prefix for the current prompt of the specified length.
+
+        Args:
+            current_prefix_length (int): The desired length of the prefix.
+        """
+
+        # Prefix of the current prompt being generated
+        current_prefix: str = self.prefix
+
+        # Get the difference in length between the existing
+        # prefix and the desired prefix length
+
+        current_prefix_tokens = self.tokenizer.encode(current_prefix)
+        current_prefix_length = len(current_prefix_tokens)
+        prefix_length_diff: int = prefix_length - current_prefix_length
+
+        # Generate the prefix if it hasn't been created yet, or add
+        # to its length if it's not long enough
+        if prefix_length_diff > 0:
+            self.prefix += self._sample_prefix(prefix_length_diff)
+            current_prefix = self.prefix
+
+        elif prefix_length_diff < 0:
+            # If the prefix is longer than needed, truncate it
+            current_prefix = self.tokenizer.decode(
+                current_prefix_tokens[:prefix_length]
+            )
+        return current_prefix
+
     def _sample_text(self, num_input_tokens: Optional[int]) -> str:
         """
         Samples text from a list of lines based on the specified number of
@@ -176,16 +241,40 @@ def _sample_text(self, num_input_tokens: Optional[int]) -> str:
         Args:
             num_input_tokens (int): The target number of input tokens.
 
+        Raises:
+            ValueError: if the prompt length is shorter than the prefix
+                length.
+
         Returns:
             str: A text prompt containing the desired number of tokens.
         """
         if not num_input_tokens:
             return random.choice(self.data)
 
+        # Calculate actual prefix length based on ratio or fixed length
+        current_prefix_length = 0
+        if self.prompt_prefix_ratio > 0.0:
+            current_prefix_length = round(num_input_tokens * self.prompt_prefix_ratio)
+
         data_copy = self.data.copy()
-        prompt = ""
-        left_tokens_to_sample = num_input_tokens
 
+        if not self.data:
+            raise ValueError("Cannot sample text from an empty dataset")
+
+        if num_input_tokens <= current_prefix_length:
+            raise ValueError("Prefix length must be shorter than total input length")
+
+        # Get the prompt prefix
+        current_prefix: str = self._get_current_prefix(current_prefix_length)
+
+        # Prepend the prefix to all prompts with a randomly picked 4 digits
+        prompt = f"{current_prefix}{random.randint(1000,9999)}"
+
+        prompt_tokens = self.tokenizer.encode(prompt)
+        left_tokens_to_sample = num_input_tokens - len(prompt_tokens)
+
+        if left_tokens_to_sample < 0:
+            return self.tokenizer.decode(prompt_tokens[:num_input_tokens])
         while left_tokens_to_sample > 0:
             random.shuffle(data_copy)
             for line in data_copy:

tests/sampling/test_text.py

@@ -221,6 +221,11 @@ def mock_encode(text, add_special_tokens=False):
             # Count actual tokens in result
             # Need to handle mixed content (original lines + decoded text)
             total_tokens = 0
+
+            # All prompts start with 4 numbers, which are 1 token
+            total_tokens += 1
+            result = result[4:]
+
             # Split by our test lines to count tokens properly
             remaining = result
             for line in self.test_data:
@@ -255,6 +260,88 @@ def test_sample_text_truncation(self):
         _ = self.sampler._sample_text(requested_tokens)
 
         # Verify decode was called with truncated tokens
-        self.tokenizer.decode.assert_called_with(
-            line_tokens[:requested_tokens], skip_special_tokens=True
+        self.tokenizer.decode.assert_called_with(line_tokens[:requested_tokens])
+
+    def test_sample_chat_prefix_ratio_request(self):
+        """Test prefix generation using ratio."""
+
+        # Mock encode to return list with length equal to number of characters in input
+        def mock_encode(text, add_special_tokens=False):
+            return [1] * len(text)
+
+        self.tokenizer.encode = mock_encode
+
+        # Mock decode to return the original text
+        def mock_decode(tokens):
+            if isinstance(tokens, list):
+                return "a" * len(tokens)  # Return 'a' repeated for the token count
+            return "decoded_text"
+
+        self.tokenizer.decode = mock_decode
+
+        scenario = NormalDistribution(
+            mean_input_tokens=20,
+            stddev_input_tokens=0,
+            mean_output_tokens=20,
+            stddev_output_tokens=0,
+        )
+        prefix_sampler = TextSampler(
+            tokenizer=self.tokenizer,
+            model=self.model,
+            output_modality=self.output_modality,
+            data=self.test_data,
+            use_scenario=True,
+            prompt_prefix_ratio=0.5,  # 50% of 20 tokens = 10 tokens
+        )
+        result = prefix_sampler.sample(scenario)
+        self.assertIsInstance(result, UserChatRequest)
+        self.assertEqual(result.model, self.model)
+        self.assertTrue(isinstance(result.prompt, str))
+        self.assertGreater(len(result.prompt), 0)
+        self.assertTrue(result.prompt.startswith(prefix_sampler.prefix))
+        self.assertEqual(len(result.prompt), 20)
+
+    def test_short_prompt_request(self):
+        """Test that short prompts are handled correctly."""
+
+        def mock_encode(text, add_special_tokens=False):
+            return [1] * len(text)
+
+        self.tokenizer.encode = mock_encode
+
+        # Mock decode to return the original text
+        def mock_decode(tokens):
+            if isinstance(tokens, list):
+                return "a" * len(tokens)  # Return 'a' repeated for the token count
+            return "decoded_text"
+
+        self.tokenizer.decode = mock_decode
+
+        self.sampler.data = ["2"]
+
+        # Scenario asks for only 1 input token
+        scenario = NormalDistribution(1, 0, 1, 0)
+
+        result = self.sampler.sample(scenario)
+        self.assertIsInstance(result, UserChatRequest)
+        # The prompt will be the 4-digit number, truncated to 1 char
+        self.assertEqual(len(result.prompt), 1)
+        self.assertGreater(len(result.prompt), 0)
+
+    def test_empty_dataset(self):
+        """Test sampling from an empty dataset."""
+        empty_sampler = TextSampler(
+            tokenizer=self.tokenizer,
+            model=self.model,
+            output_modality=self.output_modality,
+            data=[],
+            use_scenario=True,
+        )
+        scenario = NormalDistribution(10, 0, 10, 0)
+
+        with self.assertRaises(ValueError) as context:
+            empty_sampler.sample(scenario)
+
+        self.assertEqual(
+            str(context.exception), "Cannot sample text from an empty dataset"
         )