Implementation:Huggingface Transformers Pipeline Forward

Knowledge Sources	Transformers Transformers Docs
Domains	NLP, Inference, Deep Learning
Last Updated	2026-02-13 00:00 GMT

Overview

Concrete tool for executing autoregressive text generation on preprocessed inputs provided by the HuggingFace Transformers library.

Description

The TextGenerationPipeline._forward() method is the core inference step that takes preprocessed tensor inputs and invokes the model's generate() method to produce token sequences. It orchestrates the following operations:

• Input extraction: Retrieves input_ids, attention_mask, and prompt_text from the model inputs dictionary.
• Empty prompt handling: Supports unconditional generation by setting input_ids to None when the prompt is empty.
• Prefix length adjustment: When a prefix was prepended during preprocessing, adjusts max_length and min_length to compensate for the extra prefix tokens, ensuring the user's intended generation length is preserved.
• Generation config management: Applies the pipeline's default GenerationConfig unless the user provides a custom one in the keyword arguments.
• Model invocation: Calls self.model.generate() with the prepared inputs and generation parameters.
• Output reshaping: Reshapes the generated sequences from a flat batch [out_b, seq_len] to [in_b, num_return_sequences, seq_len].
• Auxiliary output collection: Extracts and reshapes any additional model outputs (attention weights, hidden states, scores) from the ModelOutput object.

Usage

This method is called automatically by the pipeline's __call__ method between preprocessing and postprocessing. Use it directly when you need:

• Fine-grained control over the generation step without postprocessing.
• Access to raw generated token IDs before decoding.
• Inspection of auxiliary model outputs (attention, hidden states).

Code Reference

Source Location

• Repository: transformers
• File: src/transformers/pipelines/text_generation.py (lines 363-424)

Signature

def _forward(self, model_inputs, **generate_kwargs):

Import

from transformers import pipeline

# _forward is a method on TextGenerationPipeline instances:
generator = pipeline("text-generation", model="gpt2")
# Access: generator._forward(model_inputs, **generate_kwargs)

I/O Contract

Inputs

Name	Type	Required	Description
model_inputs	dict	Yes	Dictionary produced by preprocess() containing: input_ids (torch.Tensor), attention_mask (torch.Tensor), and prompt_text (str or Chat).
**generate_kwargs	dict	No	Generation parameters forwarded to model.generate(). Common keys include max_new_tokens, temperature, top_k, top_p, do_sample, num_beams, num_return_sequences, generation_config.

Outputs

Name	Type	Description
model_outputs	dict	Dictionary containing: generated_sequence (torch.Tensor of shape [in_b, num_return_sequences, seq_len]), input_ids (torch.Tensor or None), prompt_text (str or Chat). Optionally includes additional_outputs (dict of reshaped auxiliary outputs such as attention scores or hidden states).

Usage Examples

Basic Usage

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")

# Step 1: Preprocess
inputs = generator.preprocess("The quick brown fox")

# Step 2: Forward pass
model_outputs = generator._forward(inputs, max_new_tokens=20)

print(model_outputs["generated_sequence"].shape)
# torch.Size([1, 1, seq_len])  -- 1 input, 1 return sequence

print(model_outputs["prompt_text"])
# "The quick brown fox"

Multiple Return Sequences

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
inputs = generator.preprocess("Once upon a time")

model_outputs = generator._forward(
    inputs,
    max_new_tokens=30,
    num_return_sequences=3,
    do_sample=True,
    temperature=0.8,
)

print(model_outputs["generated_sequence"].shape)
# torch.Size([1, 3, seq_len])  -- 1 input, 3 return sequences

Related Pages

Implements Principle

• Principle:Huggingface_Transformers_Pipeline_Forward_Pass