Implementation:Ggml org Ggml Gpt2 eval

Template:Implementation

Summary

gpt2_eval is the core evaluation function in the GGML GPT-2 backend example. It executes a single forward pass of the GPT-2 model for a batch of input tokens, producing logits that drive autoregressive text generation.

API Signature

bool gpt2_eval(
    const gpt2_model & model,
    ggml_gallocr_t allocr,
    const int n_threads,
    const int n_past,
    const std::vector<gpt_vocab::id> & embd_inp,
    std::vector<float> & embd_w
)

Source: examples/gpt-2/main-backend.cpp:L732-784

Parameters

Parameter	Type	Description
model	const gpt2_model &	The loaded GPT-2 model containing weights, hyperparameters, and KV cache tensors.
allocr	ggml_gallocr_t	Graph allocator used to reserve memory for the computation graph.
n_threads	int	Number of CPU threads to use during graph computation.
n_past	int	Context offset indicating how many tokens have already been processed (used for KV cache positioning).
embd_inp	const std::vector<gpt_vocab::id> &	Input token IDs for the current evaluation batch.
embd_w	std::vector<float> &	Output vector populated with logits for the last token position (size n_vocab).

Return Value

Returns bool -- true on success, false on failure. On success, embd_w is populated with the logit vector of size n_vocab corresponding to the last input token position.

Internal Flow

The function proceeds through the following steps:

1. Build the computation graph -- Calls gpt2_graph(model, allocr, embd_inp, n_past) to construct the GGML computation graph for the forward pass.
2. Allocate graph memory -- Invokes ggml_gallocr_alloc_graph(allocr, gf) to assign memory for all intermediate tensors in the graph.
3. Set input tensors -- Writes the input data into the graph's input tensors:
   • embd -- the token IDs from embd_inp.
   • position -- positional indices starting from n_past.
4. Compute the graph -- Executes ggml_backend_graph_compute(model.backend, gf) to run the forward pass on the configured backend.
5. Read output logits -- Extracts the logit vector from the final tensor via ggml_backend_tensor_get, writing results into embd_w.

Main Generation Loop

The autoregressive generation loop is located at examples/gpt-2/main-backend.cpp:L868-925. It orchestrates the full text generation process:

1. Prompt evaluation -- The initial prompt tokens are passed to gpt2_eval to populate the KV cache.
2. Token-by-token generation -- On each iteration:
   • gpt2_eval is called with the most recently generated token and the current n_past offset.
   • The returned logits in embd_w are fed to the sampler to select the next token.
   • The selected token is appended to the context and n_past is incremented.
3. Stopping conditions -- The loop terminates when the maximum token count is reached or the end-of-text token is emitted.

Related

• Principle:Ggml_org_Ggml_Autoregressive_Generation
• Environment:Ggml_org_Ggml_C_Cpp_Build_Environment
• Heuristic:Ggml_org_Ggml_Thread_Count_Selection

Source

• GGML