Merge branch 'helenn-refactor-textgen' into 'main'

MCore Inference misc changes

See merge request ADLR/megatron-lm!2439

examples/inference/README.md

@@ -1,5 +1,5 @@
 ### Megatron Core Inference Documentation
-This guide will walk you through how you can use megatron core for inference on your models. 
+This guide provides an example for Megatron Core for running model inference. 
 
 ### Contents
 - [Megatron Core Inference Documentation](#megatron-core-inference-documentation)
@@ -18,21 +18,21 @@ This guide will walk you through how you can use megatron core for inference on
 <br>
 
 #### 1. Quick Start
-This will walk you through the flow of running batch inference on a GPT model trained using megatron core. The file can be found at [simple_gpt_batch_inference.py](./gpt/simple_gpt_batch_inference.py)
+This example runs batch inference on a GPT model trained using Megatron Core. The entrypoint is [simple_gpt_batch_inference.py](./gpt/gpt_batch_inference.py)
 
 <br>
 
-##### 1.1 Understanding The Code
-***STEP 1 - We initialize model parallel and other default arguments***
-We can default micro batch size to be 1, since for TP models it is not used, and for PP models it is calculated during runtime. 
+##### 1.1 Code Walkthrough 
+***STEP 1 - Initialize model parallel and other default arguments***
+The micro batch size is set as 1 as it is not used in tensor-parallelism only, and for pipeline-parallel models it is calculated at runtime. 
 ```python
     initialize_megatron(
         args_defaults={'no_load_rng': True, 'no_load_optim': True, 'micro_batch_size': 1}
     )
 ```
 
-***STEP 2 - We load the model using the model_provider_function***
-NOTE: The model provider function in the script supports MCore and Legacy models. 
+***STEP 2 - Load the model using the model_provider_function***
+NOTE: The model provider function supports both MCore and Legacy models. 
 
 ```python
     model = get_model(model_provider, wrap_with_ddp=False)
@@ -41,10 +41,10 @@ NOTE: The model provider function in the script supports MCore and Legacy models
 ```
 
 ***STEP 3 - Choose an engine***
-One of the important elements of the generate function is an inference engine. In this example we will be choosing the [megatron core engine](../../megatron/core/inference/engine/mcore_engine.py) with a [simple text generation controller](../../megatron/core/inference/text_generation_controllers/simple_text_generation_controller.py), the default engine. Other engines that will be supported in the future are TRTLLMEngine.
+Text generation requires an inference engine, which includes a scheduler. The default engine is the [Megatron Core engine](../../megatron/core/inference/engine/mcore_engine.py) with a simple [text generation controller](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py). TRTLLMEngine will be supported in the future.
 ```python
     inference_wrapped_model = GPTInferenceWrapper(model, args)
-    text_generation_controller = SimpleTextGenerationController(
+    text_generation_controller = TextGenerationController(
         inference_wrapped_model=inference_wrapped_model, 
         tokenizer=tokenizer
     )
@@ -53,12 +53,12 @@ One of the important elements of the generate function is an inference engine. I
     )
 ```
 
-***STEP 4 - Run the generate function and display results***
-We use default values for the [common inference params](../../megatron/core/inference/common_inference_params.py). Customize this if you want to change top_p, top_k, number of tokens to generate etc. 
-*Note that the result is returned as a list of [InferenceRequests](../../megatron/core/inference/inference_request.py)*
+***STEP 4 - Run text generation***
+The [SamplingParams](../../megatron/core/inference/sampling_params.py) contains suggested defaults. Customize this to change top_p, top_k, number of tokens to generate etc. 
+*Note: The result is returned as a list of [InferenceRequests](../../megatron/core/inference/inference_request.py)*
 ```python
     results: List[InferenceRequest] = inference_engine.generate(
-        prompts=args.prompts, common_inference_params=common_inference_params
+        prompts=args.prompts, sampling_params=sampling_params
     )
 
     if torch.distributed.get_rank() == 0:
@@ -76,12 +76,12 @@ We use default values for the [common inference params](../../megatron/core/infe
 <br>
 
 ##### 1.2 Running The Code
-An example run script is shown below. Change the tokenizer paths, inference params, and other settings for your model. 
+An example run script is shown below. Set the tokenizer paths, inference params, and other settings appropriately. 
 
-For a quick recap on inference params refer to [this blog](https://ivibudh.medium.com/a-guide-to-controlling-llm-model-output-exploring-top-k-top-p-and-temperature-parameters-ed6a31313910) 
+For a quick recap on sampling parameters, refer to [this blog](https://ivibudh.medium.com/a-guide-to-controlling-llm-model-output-exploring-top-k-top-p-and-temperature-parameters-ed6a31313910).
 
 ```
-#In a slurm cluster (You could also use docker)
+# In a slurm cluster (You could also use docker)
 ACCOUNT=<account>
 MLM_PATH=/path/to/megatron-lm
 GPT_CKPT=/path/to/gpt/ckpt
@@ -133,25 +133,26 @@ NOTE: Other parameters which can be customized for inference are :-
 --top_p (top_p sampling)
 --num-tokens-to-generate (Number of tokens to generate for each prompt)
 --inference-batch-times-seqlen-threshold (During inference, if batch-size times sequence-length is smaller than this threshold then we will not use pipelining, otherwise we will.')
---use-dist-ckpt (If you are using dist checkpoint format for the model)
---use-legacy-models (If you are using legacy gpt model instead of mcore gpt model)
+--use-dist-ckpt (If using dist checkpoint format for the model)
+--use-legacy-models (If using legacy gpt model instead of mcore gpt model)
 
 ```
 
 
 <br>
 
 
-#### 2. Flow of Control In MCore Backend
-The following is what happens in the [simple_gpt_batch_inference.py](./gpt/simple_gpt_batch_inference.py).
-* We call  [mcore_engine](../../megatron/core/inference/engines/mcore_engine.py) **generate()** function with all our input prompts.
-* The scheduler in the engine will add these prompts to the [active requests] pool (../../megatron/core/inference/inference_request.py) until we hit the max batch size, and then it will put the rest in the waiting requests pool. 
-* The engine will then run until all requests (waiting + active) are completed 
+#### 2. Control Flow in the MCore Backend
+An example of inference with static batching is provided in [gpt_batch_inference.py](./gpt/gpt_batch_inference.py).
+* [mcore_engine](../../megatron/core/inference/engines/mcore_engine.py) **generate()** function is called with the input prompts.
+* The `Scheduler` in the engine will add these prompts to the [active requests] pool (../../megatron/core/inference/inference_request.py) until max batch size is hit. Remaining requests will be added to the waiting requests pool. 
+* The engine will run until all requests (waiting + active) are completed. 
     * The active requests are passed into  **generate_all_output_tokens_static_batch()** of the text generation controller . 
-    * This function uses the [model_inference_wrappers](../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) **prep_model_for_inference()** , and then runs an auto regressive loop
-    * In the auto regressive loop, the **get_batch_for_context_window()** method of the inference wrapper is called to get the required input, passes it into the **run_one_forward_step()** method, which calls the appropriate (PP, TP) model `.forward()` methods to get the output logits
-    * The output logits are synchronized across all pipeline parallel ranks
-    * The text generation controller obtains the log probabilities and samples tokens based on the strategy defined in the common inference parameters.
+    * This function uses the **prep_model_for_inference()** method of the [model_inference_wrappers](../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) and runs an autoregressive sampling loop
+    * In the autoregressive loop, the **get_batch_for_context_window()** method of the inference wrapper is called to slice out the input tokens and masks
+    * Input tokens and masks are passed it into the **run_one_forward_step()** method, which calls the model `.forward()` method to get the output logits
+    * Output logits are synchronized across all pipeline parallel ranks
+    * The text generation controller obtains the log probabilities and samples tokens based on the strategy defined in the sampling parameters.
     * The sampled tokens are then appended to the input prompt tokens for the next iteration 
     * The **update_generation_status()** method of the text generation controller checks which prompts have finished generating or hit a stop condition
     * After the inference loop, the result is detokenized and stored as an attribute of the InferenceRequest. These requests are marked as completed. 
@@ -160,45 +161,49 @@ The following is what happens in the [simple_gpt_batch_inference.py](./gpt/simpl
 <br>
 
 #### 3. Customizing The Inference Pipeline
-The following guide will walk you through how you can customize different parts of the inference pipeline. There are three levels at which you can customize the pipeline. 
-* **Inference engine** - Highest level of customization. Currently we support the MCore Engine. Change this to add a new engine.
-* **Text generation controller** - Extend this to customize tokenization, detokenization, or implement a new sampling strategy.
+
+The inference pipeline supports three levels of customization:
+
+* **Inference engine** - The MCore Engine is currently supported. Change this to add a new backend.
+* **Text generation controller** - The main sampling loop. This can be customized to support alternative tokenization, detokenization, or to implement a new sampling strategy.
 * **Inference Wrapped Model** - Change this to support a new model.
 * **Modify Inference Parameters** - Change this to update top_p, top_k, number of tokens to be generated, temperature, or other sampling parameters.
 
 <br>
 
 ##### 3.1. Create Your Own Inference Backend 
-This is the highest level of customization. The  [abstract_engine.py](./../../megatron/core/inference/engine/abstract_engine.py) file has a generate method that can be extended to support a new backend. 
+The  [abstract_engine.py](./../../megatron/core/inference/engine/abstract_engine.py) file contains a `generate` method that can be extended to support a new backend. 
 
 ```python
 class AbstractEngine(ABC):
     @staticmethod
     def generate(self) -> dict:
         """The abstract backend's generate function. 
 
-        To define your own backend, make sure you implement this and return the outputs as a dictionary . 
-
+        To define a new backend, implement this method and return the outputs as a dictionary. 
+```
 
 <br>
 
-##### 3.2. Create Your Own Text Generation Controller
-In case you want to use the megatron core backend, but would like to overwrite the tokenization, text generation or detokenization extend the [simple_text_generation_controller.py](../../megatron/core/inference/text_generation_controllers/simple_text_generation_controller.py). The class has the following methods
+##### 3.2. Implement a new Sampling Loop 
+
+The [TextGenerationController](../../megatron/core/inference/text_generation_controllers/text_generation_controller.py) contains the main sampling loop and can be modified to support new tokenization, detokenization, or sampling strategies.
+
 ``` python
-class SimpleTextGenerationController:
+class TextGenerationController:
 
     def tokenize_prompt(self, prompt: str) -> Tuple[torch.Tensor, torch.Tensor]:
         """Utility to tokenize the input prompts"""
 
     def sample_from_logits(
         self,
         last_token_logits: torch.Tensor,
-        common_inference_params: CommonInferenceParams,
+        sampling_params: SamplingParams,
         vocab_size: int,
     ) -> torch.Tensor:
         """Samples the logits to generate outputs
 
-        Given the logits of the last token, this function samples it according to the parameters defined in common_inference_params and returns the samples
+        Given the logits of the last token, this function samples according to the parameters defined in sampling_params and returns the sampled tokens.
         """
 
     def update_generation_status(
@@ -229,12 +234,12 @@ class SimpleTextGenerationController:
 <br>
 
 ##### 3.3. Support Other Models
-In order to support other models please extend the [abstract_model_inference_wrapper.py](./../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) file. The abstract wrapper already supports the following :
-* Forward method which automatically calls the appropriate forward method (PP or TP etc) depending on model parallel settings
-* Initalizes the model and puts it in eval mode
-* Obtains the input parameters (batch size, max seq length) and has an instance of the input 
+Extend [abstract_model_inference_wrapper.py](./../../megatron/core/inference/model_inference_wrappers/abstract_model_inference_wrapper.py) to support other models. The abstract model wrapper implements: 
+* Forward method which calls the model `forward` method depending on model parallel settings
+* Initializes the model and puts it in `.eval()` mode
+* Setup for the input parameters (max batch size, max seq length) 
 
-The main methods to change for your model might be the following: 
+The following methods should be implemented: 
 ```python
 class AbstractModelInferenceWrapper:
     def prep_model_for_inference(self, prompts_tokens: torch.Tensor):
@@ -247,28 +252,28 @@ class AbstractModelInferenceWrapper:
     def get_batch_for_context_window(self) -> List:
         """Returns the input data for inference 
 
-        This function gets called iteratively in the inference loop . It can be used to extract relevant input from the prompt tokens, attention mask etc. required for each step in inference.
+        This function gets called iteratively in the inference loop. It can be used to extract relevant input from the prompt tokens, attention mask etc. required for each step in inference.
 ```
 
-Refer to [gpt_inference_wrapper.py](../../megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py) for an example of extending this for GPTModel.
+Refer to [gpt_inference_wrapper.py](../../megatron/core/inference/model_inference_wrappers/gpt/gpt_inference_wrapper.py) for an example of implementing this for GPTModel.
 
 <br>
 
 ##### 3.3. Modify Inference Parameters
-We use  [common inference params](../../megatron/core/inference/common_inference_params.py) for text generation. Customize this if you want to change top_p, top_k, number of tokens to generate etc. If you want to add other attributes that you would use in the inference loop, you can do that as shown below
+We use  [common inference params](../../megatron/core/inference/sampling_params.py) for text generation. Customize this if you want to change top_p, top_k, number of tokens to generate etc. If you want to add other attributes that you would use in the inference loop, you can do that as shown below
 
 ```
-from megatron.core.inference.common_inference_params import CommonInferenceParams
+from megatron.core.inference.sampling_params import SamplingParams
 
-c = CommonInferenceParams(temperature=0.5)
+c = SamplingParams(temperature=0.5)
 c.add_attributes({'min_length':4, 'eod_id':153})
 ```
 
 <br>
 
 #### 4. Future work
-The following are planned for the future releases . 
+The following features are planned for the future releases. 
 * Dynamic batching 
 * Paged Attention
 * TRTLLM Engine support
-* Support for Multimodal model inference
+* Support for multimodal inference

examples/inference/gpt/simple_gpt_batch_inference.py → examples/inference/gpt/gpt_batch_inference.py

@@ -6,10 +6,10 @@
 from argparse import Namespace
 from megatron.core.inference.engines.abstract_engine import AbstractEngine
 from megatron.core.inference.engines.mcore_engine import MCoreEngine
-from megatron.core.inference.common_inference_params import CommonInferenceParams
+from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import GPTInferenceWrapper
 from megatron.core.inference.inference_request import InferenceRequest
-from megatron.core.inference.text_generation_controllers.simple_text_generation_controller import SimpleTextGenerationController
+from megatron.core.inference.text_generation_controllers.text_generation_controller import TextGenerationController
 from megatron.core.transformer.module import MegatronModule
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
                                              os.path.pardir, os.path.pardir)))
@@ -66,7 +66,7 @@ def get_inference_engine(args: Namespace, model: MegatronModule) -> AbstractEngi
     )
 
     inference_wrapped_model = GPTInferenceWrapper(model, inference_wrapper_config)
-    text_generation_controller = SimpleTextGenerationController(inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer)
+    text_generation_controller = TextGenerationController(inference_wrapped_model=inference_wrapped_model, tokenizer=tokenizer)
     return MCoreEngine(text_generation_controller=text_generation_controller, max_batch_size=args.max_batch_size)
 
 def main():
@@ -89,15 +89,15 @@ def main():
 
     inference_engine = get_inference_engine(args, model)
 
-    common_inference_params = CommonInferenceParams(
+    sampling_params = SamplingParams(
         temperature=args.temperature, 
         top_k=args.top_k, 
         top_p=args.top_p, 
         return_log_probs=args.return_log_probs, 
         num_tokens_to_generate=args.num_tokens_to_generate)
 
     results: List[InferenceRequest] = inference_engine.generate(
-        prompts=args.prompts, common_inference_params=common_inference_params
+        prompts=args.prompts, sampling_params=sampling_params
     )
 
     if torch.distributed.get_rank() == 0:

examples/inference/t5/simple_t5_batch_inference.py

@@ -5,7 +5,7 @@
 import torch
 
 import pretrain_t5
-from megatron.core.inference.common_inference_params import CommonInferenceParams
+from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.inference.engines.abstract_engine import AbstractEngine
 from megatron.core.inference.engines.mcore_engine import MCoreEngine
 from megatron.core.inference.inference_request import InferenceRequest
@@ -120,7 +120,7 @@ def main():
 
     inference_engine = get_inference_engine(args, model)
 
-    common_inference_params = CommonInferenceParams(
+    sampling_params = SamplingParams(
         temperature=args.temperature,
         top_k=args.top_k,
         top_p=args.top_p,
@@ -138,7 +138,7 @@ def main():
         prompts=args.prompts,
         add_BOS=True,
         encoder_prompts=args.encoder_prompts,
-        common_inference_params=common_inference_params,
+        sampling_params=sampling_params,
     )
 
     if torch.distributed.get_rank() == 0: