Llm generate upgrade

docs/sections/how_to_guides/basic/llm/index.md

@@ -7,20 +7,45 @@ LLM subclasses are designed to be used within a [Task][distilabel.steps.tasks.Ta
 ```python
 from distilabel.models import InferenceEndpointsLLM
 
-llm = InferenceEndpointsLLM(model="meta-llama/Meta-Llama-3.1-70B-Instruct")
+llm = InferenceEndpointsLLM(
+    model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
+    tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct"
+)
 llm.load()
 
 llm.generate_outputs(
     inputs=[
         [{"role": "user", "content": "What's the capital of Spain?"}],
     ],
 )
-# "The capital of Spain is Madrid."
+# [
+#   {
+#     "generations": [
+#       "The capital of Spain is Madrid."
+#     ],
+#     "statistics": {
+#       "input_tokens": [
+#         43
+#       ],
+#       "output_tokens": [
+#         8
+#       ]
+#     }
+#   }
+# ]
 ```
 
-!!! NOTE
+!!! Note
     Always call the `LLM.load` or `Task.load` method when using LLMs standalone or as part of a `Task`. If using a `Pipeline`, this is done automatically in `Pipeline.run()`.
 
+!!! Tip "New in version 1.5.0"
+    Since version `1.5.0` the LLM output is a list of dictionaries (one per item in the `inputs`),
+    each containing `generations`, that reports the text returned by the `LLM`, and a `statistics` field that will store statistics related to the `LLM` generation. Initially, this will include
+    `input_tokens` and `output_tokens` when available, which will be obtained via the API when available, or if a tokenizer is available for the model used, using the tokenizer for the model.
+    This data will be moved by the corresponding `Task` during the pipeline processing and moved to `distilabel_metadata` so we can operate on this data if we want, like for example computing the number of tokens per dataset.
+
+    To access to the previous result one just has to access to the generations in the resulting dictionary: `result[0]["generations"]`.
+
 ### Offline Batch Generation
 
 By default, all `LLM`s will generate text in a synchronous manner i.e. send inputs using `generate_outputs` method that will get blocked until outputs are generated. There are some `LLM`s (such as [OpenAILLM][distilabel.models.llms.openai.OpenAILLM]) that implements what we denote as _offline batch generation_, which allows to send the inputs to the LLM-as-a-service which will generate the outputs asynchronously and give us a job id that we can use later to check the status and retrieve the generated outputs when they are ready. LLM-as-a-service platforms offers this feature as a way to save costs in exchange of waiting for the outputs to be generated.
@@ -56,7 +81,8 @@ llm.generate_outputs(  # (4)
         [{"role": "user", "content": "What's the capital of Spain?"}],
     ],
 )
-# "The capital of Spain is Madrid."
+# [{'generations': ['The capital of Spain is Madrid.'],
+#   'statistics': {'input_tokens': [13], 'output_tokens': [7]}}]
 ```
 
 1. At first the `jobs_ids` attribute is `None`.
@@ -81,7 +107,8 @@ llm.generate_outputs(
         [{"role": "user", "content": "What's the capital of Spain?"}],
     ],
 )
-# "The capital of Spain is Madrid."
+# [{'generations': ['The capital of Spain is Madrid.'],
+#   'statistics': {'input_tokens': [13], 'output_tokens': [7]}}]
 ```
 
 ### Within a Task
@@ -92,20 +119,30 @@ Pass the LLM as an argument to the [`Task`][distilabel.steps.tasks.Task], and th
 from distilabel.models import OpenAILLM
 from distilabel.steps.tasks import TextGeneration
 
-llm = OpenAILLM(model="gpt-4")
+llm = OpenAILLM(model="gpt-4o-mini")
 task = TextGeneration(name="text_generation", llm=llm)
 
 task.load()
 
 next(task.process(inputs=[{"instruction": "What's the capital of Spain?"}]))
-# [{'instruction': "What's the capital of Spain?", "generation": "The capital of Spain is Madrid."}]
+# [{'instruction': "What's the capital of Spain?",
+#   'generation': 'The capital of Spain is Madrid.',
+#   'distilabel_metadata': {'raw_output_text_generation': 'The capital of Spain is Madrid.',
+#    'raw_input_text_generation': [{'role': 'user',
+#      'content': "What's the capital of Spain?"}],
+#    'statistics_text_generation': {'input_tokens': 13, 'output_tokens': 7}},
+#   'model_name': 'gpt-4o-mini'}]
 ```
 
+!!! Note
+    As mentioned in *Working with LLMs* section, the generation of an LLM is automatically moved to `distilabel_metadata` to avoid interference with the common workflow, so the addition of the `statistics` it's an extra component available for the user, but nothing has to be changed in the
+    defined pipelines.
+
 ### Runtime Parameters
 
 LLMs can have runtime parameters, such as `generation_kwargs`, provided via the `Pipeline.run()` method using the `params` argument.
 
-!!! NOTE
+!!! Note
     Runtime parameters can differ between LLM subclasses, caused by the different functionalities offered by the LLM providers.
 
 ```python
@@ -122,7 +159,7 @@ with Pipeline(name="text-generation-pipeline") as pipeline:
 
     text_generation = TextGeneration(
         name="text_generation",
-        llm=OpenAILLM(model="gpt-4"),
+        llm=OpenAILLM(model="gpt-4o-mini"),
     )
 
     load_dataset >> text_generation
@@ -200,9 +237,12 @@ To create custom LLMs, subclass either [`LLM`][distilabel.models.llms.LLM] for s
 
 `generate` and `agenerate` keyword arguments (but `input` and `num_generations`) are considered as `RuntimeParameter`s, so a value can be passed to them via the `parameters` argument of the `Pipeline.run` method.
 
-!!! NOTE
+!!! Note
     To have the arguments of the `generate` and `agenerate` coerced to the expected types, the `validate_call` decorator is used, which will automatically coerce the arguments to the expected types, and raise an error if the types are not correct. This is specially useful when providing a value for an argument of `generate` or `agenerate` from the CLI, since the CLI will always provide the arguments as strings.
 
+!!! Warning
+    Additional LLMs created in `distilabel` will have to take into account how the `statistics` are generated to properly include them in the LLM output.
+
 ## Available LLMs
 
 [Our LLM gallery](../../../../components-gallery/llms/index.md) shows a list of the available LLMs that can be used within the `distilabel` library.

docs/sections/how_to_guides/basic/task/index.md

@@ -21,26 +21,35 @@ task.load()
 
 next(task.process([{"instruction": "What's the capital of Spain?"}]))
 # [
-#     {
-#         'instruction': "What's the capital of Spain?",
-#         'generation': 'The capital of Spain is Madrid.',
-#         'distilabel_metadata': {
-#               'raw_output_text-generation': 'The capital of Spain is Madrid.',
-#               'raw_input_text-generation': [
-#                   {'role': 'user', 'content': "What's the capital of Spain?"}
-#               ]
-#         },
-#         'model_name': 'meta-llama/Meta-Llama-3-70B-Instruct'
-#     }
+#   {
+#     "instruction": "What's the capital of Spain?",
+#     "generation": "The capital of Spain is Madrid.",
+#     "distilabel_metadata": {
+#       "raw_output_text-generation": "The capital of Spain is Madrid.",
+#       "raw_input_text-generation": [
+#         {
+#           "role": "user",
+#           "content": "What's the capital of Spain?"
+#         }
+#       ],
+#       "statistics_text-generation": {  # (1)
+#         "input_tokens": 18,
+#         "output_tokens": 8
+#       }
+#     },
+#     "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct"
+#   }
 # ]
 ```
 
-!!! NOTE
+1. The `LLMs` will not only return the text but also a `statistics_{STEP_NAME}` field that will contain statistics related to the generation. If available, at least the input and output tokens will be returned.
+
+!!! Note
     The `Step.load()` always needs to be executed when being used as a standalone. Within a pipeline, this will be done automatically during pipeline execution.
 
 As shown above, the [`TextGeneration`][distilabel.steps.tasks.TextGeneration] task adds a `generation` based on the `instruction`.
 
-!!! Tip
+!!! Tip "New in version 1.2.0"
     Since version `1.2.0`, we provide some metadata about the LLM call through `distilabel_metadata`. This can be disabled by setting the `add_raw_output` attribute to `False` when creating the task.
 
     Additionally, since version `1.4.0`, the formatted input can also be included, which can be helpful when testing
@@ -57,9 +66,12 @@ As shown above, the [`TextGeneration`][distilabel.steps.tasks.TextGeneration] ta
     )
     ```
 
+!!! Tip "New in version 1.5.0"
+    Since version `1.5.0` `distilabel_metadata` includes a new `statistics` field out of the box. The generation from the LLM will not only contain the text, but also statistics associated with the text if available, like the input and output tokens. This field will be generated with `statistic_{STEP_NAME}` to avoid collisions between different steps in the pipeline, similar to how `raw_output_{STEP_NAME}` works.
+
 ### Task.print
 
-!!! Info
+!!! Info "New in version 1.4.0"
     New since version `1.4.0`, [`Task.print`][distilabel.steps.tasks.base._Task.print] `Task.print` method.
 
 The `Tasks` include a handy method to show what the prompt formatted for an `LLM` would look like, let's see an example with [`UltraFeedback`][distilabel.steps.tasks.ultrafeedback.UltraFeedback], but it applies to any other `Task`.
@@ -271,3 +283,6 @@ We can define a custom step by creating a new subclass of the [`Task`][distilabe
         # Format the `LLM` output here
         return {"output_field": output}
     ```
+
+!!! Warning
+    Most `Tasks` reuse the `Task.process` method to process the generations, but if a new `Task` defines a custom `process` method, like happens for example with [`Magpie`][distilabel.steps.tasks.magpie.base.Magpie], one hast to deal with the `statistics` returned by the `LLM`.

src/distilabel/models/llms/anthropic.py

@@ -30,13 +30,19 @@
 from distilabel.mixins.runtime_parameters import RuntimeParameter
 from distilabel.models.llms.base import AsyncLLM
 from distilabel.models.llms.typing import GenerateOutput
+from distilabel.models.llms.utils import prepare_output
 from distilabel.steps.tasks.typing import (
     FormattedInput,
     InstructorStructuredOutputType,
 )
 
 if TYPE_CHECKING:
+    from typing import BaseModel
+
     from anthropic import AsyncAnthropic
+    from anthropic.types import Message
+
+    from distilabel.llms.typing import LLMStatistics
 
 
 _ANTHROPIC_API_KEY_ENV_VAR_NAME = "ANTHROPIC_API_KEY"
@@ -260,17 +266,26 @@ async def agenerate(  # type: ignore
         if structured_output:
             kwargs = self._prepare_kwargs(kwargs, structured_output)
 
-        generations = []
-
-        completion = await self._aclient.messages.create(**kwargs)  # type: ignore
+        completion: Union["Message", "BaseModel"] = await self._aclient.messages.create(
+            **kwargs
+        )  # type: ignore
         if structured_output:
-            generations.append(completion.model_dump_json())
-            return generations
+            # raw_response = completion._raw_response
+            return prepare_output(
+                [completion.model_dump_json()],
+                **self._get_llm_statistics(completion._raw_response),
+            )
 
         if (content := completion.content[0].text) is None:
             self._logger.warning(
                 f"Received no response using Anthropic client (model: '{self.model}')."
                 f" Finish reason was: {completion.stop_reason}"
             )
-        generations.append(content)
-        return generations
+        return prepare_output([content], **self._get_llm_statistics(completion))
+
+    @staticmethod
+    def _get_llm_statistics(completion: "Message") -> "LLMStatistics":
+        return {
+            "input_tokens": [completion.usage.input_tokens],
+            "output_tokens": [completion.usage.output_tokens],
+        }

src/distilabel/models/llms/base.py

@@ -21,6 +21,7 @@
 import time
 from abc import ABC, abstractmethod
 from functools import cached_property
+from itertools import islice
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
@@ -33,7 +34,6 @@
     RuntimeParametersMixin,
 )
 from distilabel.utils.docstring import parse_google_docstring
-from distilabel.utils.itertools import grouper
 from distilabel.utils.notebook import in_notebook
 from distilabel.utils.serialization import _Serializable
 
@@ -459,18 +459,16 @@ async def _agenerate(
                 )
                 for input in inputs
             ]
-            return await asyncio.gather(*tasks)
+            result = await asyncio.gather(*tasks)
+            return result
 
         tasks = [
             asyncio.create_task(self.agenerate(input=input, **kwargs))
             for input in inputs
             for _ in range(num_generations)
         ]
-        outputs = [outputs[0] for outputs in await asyncio.gather(*tasks)]
-        return [
-            list(group)
-            for group in grouper(outputs, n=num_generations, incomplete="ignore")
-        ]
+        outputs = await asyncio.gather(*tasks)
+        return merge_responses(outputs, n=num_generations)
 
     def generate(
         self,
@@ -590,3 +588,43 @@ def _prepare_kwargs(
             },
         )
         return arguments
+
+
+def merge_responses(
+    responses: List[Dict[str, Any]], n: int = 1
+) -> List[Dict[str, Any]]:
+    """Helper function to group the responses from `LLM.agenerate` method according
+    to the number of generations requested.
+
+    Args:
+        responses: the responses from the `LLM.agenerate` method.
+        n: number of responses to group together. Defaults to 1.
+
+    Returns:
+        List of merged responses, where each merged response contains n generations
+        and their corresponding statistics.
+    """
+    if not responses:
+        return []
+
+    def chunks(lst, n):
+        """Yield successive n-sized chunks from lst."""
+        for i in range(0, len(lst), n):
+            yield list(islice(lst, i, i + n))
+
+    # Split responses into groups of size n
+    grouped_responses = list(chunks(responses, n))
+
+    result = []
+    for group in grouped_responses:
+        first = group[0]
+        merged = {
+            "generations": sum((r["generations"] for r in group), []),
+            "statistics": {
+                key: sum((r["statistics"][key] for r in group), [])
+                for key in first["statistics"]
+            },
+        }
+        result.append(merged)
+
+    return result