[WIP] Multiturn mm single image

torchchat/generate.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 import argparse
+import base64
 import itertools
 import logging
 import os
@@ -12,6 +13,7 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from io import BytesIO
 from os import PathLike
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
@@ -600,9 +602,7 @@ def generate(
 
         if len(prompt.shape) > 1:
             prompt = prompt.squeeze(0)
-        T = prompt.size(0)
-        max_new_tokens = min(max_new_tokens, max_seq_length - start_pos - T)
-        T_new = T + max_new_tokens
+        prompt_length = prompt.size(0)
         # set up caches only if first inference
         if start_pos == 0:
             model = model.to(device=device)
@@ -616,7 +616,7 @@ def generate(
                         batch_size=1,
                         dtype=self.dtype,
                         encoder_max_seq_len=6404,
-                        decoder_max_seq_len=T_new,
+                        decoder_max_seq_len=max_seq_length,
                     )
                 else:
                     model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
@@ -629,7 +629,7 @@ def generate(
                 model.reset_caches()
 
         input_pos = torch.arange(
-            start_pos, T + start_pos, device=device, dtype=torch.int
+            start_pos, prompt_length + start_pos, device=device, dtype=torch.int
         )
 
         prefill_t0 = time.perf_counter()
@@ -655,7 +655,7 @@ def generate(
         # max_new_tokens <= 2 means we are effectively not calling decode_n_tokens().
         callback(next_token.clone().view(-1), done_generating=max_new_tokens <= 2)
 
-        input_pos = torch.tensor([start_pos + T], device=device, dtype=torch.int)
+        input_pos = torch.tensor([start_pos + prompt_length], device=device, dtype=torch.int)
         accept_counts = [0] * (
             speculate_k + 1
         )  # creates array of [0, 0, 0, ...] that is speculate_k + 1 long
@@ -678,7 +678,7 @@ def generate(
                 )
 
                 accept_counts[len(next_tokens) - 1] += 1
-                num_added = min(T_new - input_pos - 1, len(next_tokens))
+                num_added = min(max_new_tokens - input_pos - 1, len(next_tokens))
                 for token in next_tokens[:num_added,]:
                     callback(token)
                     yield token, None
@@ -734,13 +734,14 @@ def _callback(self, x, *, buffer, done_generating):
         if len(buffer) == 4 or done_generating:
             print("".join(buffer), end="", flush=True)
             buffer.clear()
-        # print(, end='', flush=True)
+        print(, end='', flush=True)
 
     def _gen_model_input(
         self,
         prompt: Union[str | List[Any]],
         image_prompts: Optional[List[str | Image.Image]] = None,
         max_new_tokens: Optional[int] = None,
+        max_seq_len: Optional[int] = 2048,
     ) -> Tuple[torch.Tensor, Optional[Dict[str, Any]]]:
         """
         Convert prompt and image prompts into consumable model input args.
@@ -757,7 +758,7 @@ def _gen_model_input(
             Tuple[torch.Tensor, Optional[Dict[str, Any]]]: Encoded prompt and batch config for multimodal models.
         """
 
-        # Not Llama 3.2 11B
+        # Text-Only model
         if self.model.config.model_type != ModelType.Flamingo:
             # Single String prompt
             if isinstance(prompt, str):
@@ -778,6 +779,7 @@ def _gen_model_input(
         assert (
             image_prompts is None or len(image_prompts) == 1
         ), "At most one image is supported at the moment"
+
         if image_prompts and isinstance(image_prompts[0], str):
             images = [Image.open(image_prompts[0])]
         else:
@@ -786,24 +788,41 @@ def _gen_model_input(
         assert (
             max_new_tokens is not None
         ), "max_new_tokens must be specified for Flamingo models"
-        assert isinstance(
-            prompt, str
-        ), "(Currently) prompt must be a str for Flamingo models"
-
-        is_multimodal = images is not None
-        content = [{"type": "text", "content": prompt}]
 
-        if is_multimodal:
-            content = [{"type": "image", "content": images[0]}] + content
-
-        messages = [
-            Message(
-                role="user",
-                content=content,
-                eot=True,
-            ),
-            Message(role="assistant", content=""),
-        ]
+        image_found = False
+        messages = []
+        for message in prompt:
+            if isinstance(message["content"], str):
+                messages.append(Message(**message))
+
+            elif isinstance(message["content"], list):
+                images = None
+                for content_dict in message["content"]:
+                    if content_dict["type"] == "text":
+                        prompt_arg = content_dict["text"]
+                    elif content_dict["type"] == "image_url":
+                        assert (
+                            images is None
+                        ), "At most one image is supported at the moment"
+
+                        base64_decoded = base64.b64decode(
+                            content_dict["image_url"].split(";base64,")[1]
+                        )
+                        images = [Image.open(BytesIO(base64_decoded))]
+                        image_found = True
+
+                is_multimodal = images is not None
+                content = [{"type": "text", "content": prompt_arg}]
+                []
+                if is_multimodal:
+                    content = [{"type": "image", "content": images[0]}] + content
+
+                messages.append(
+                    Message(
+                        role="user",
+                        content=content,
+                    )
+                )
 
         transform = llama3_2_vision_transform(str(self.tokenizer_args.tokenizer_path))
 
@@ -812,27 +831,37 @@ def _gen_model_input(
         with device, set_default_dtype(self.dtype):
             data = transform({"messages": messages}, inference=True)
 
-            if is_multimodal:
+            if image_found:
                 batch = padded_collate_tiled_images_and_mask(
                     [data], pad_direction="left", pad_max_images=1
                 )
                 encoded = batch.pop("tokens").to(device).view(-1)
-                seq_len = encoded.size(0)
+                seq_len = encoded.size(0) 
                 batch["encoder_mask"] = batch["encoder_mask"][:, :seq_len]
                 batch["encoder_input"]["images"] = batch["encoder_input"]["images"].to(
                     self.dtype
                 )
+
             else:
                 encoded = torch.tensor(data["tokens"], device=device).view(-1)
                 seq_len = encoded.size(0)
                 batch = {}
 
-            total_response_length = seq_len + max_new_tokens
-            batch["causal_mask"] = torch.tril(
-                torch.ones(
-                    size=(total_response_length, total_response_length),
-                    dtype=torch.bool,
-                )
+            total_response_length = max_seq_len + max_new_tokens
+            batch["causal_mask"] = torch.nn.functional.pad(
+                torch.tril(
+                    torch.ones(
+                        size=(total_response_length, total_response_length),
+                        dtype=torch.bool,
+                    )
+                ),
+                (
+                    0,
+                    max_seq_len - total_response_length,
+                    0,
+                    max_seq_len - total_response_length,
+                ),
+                value=0,
             )
 
         logging.debug(encoded)
@@ -849,6 +878,7 @@ def chat(
             generator_args.prompt,
             generator_args.image_prompts,
             generator_args.max_new_tokens,
+            generator_args.max_seq_length,
         )
 
         model_size = sum(

torchchat/usages/openai_api.py

@@ -316,38 +316,22 @@ def _gen_model_inputs_from_openai_completion_request(
         if not isinstance(self.model, FlamingoModel):
             prompt = [
                 {"role": message["role"], "content": message["content"]}
-                for message in completion_request.messages
+                for message in messages
             ]
             return self._gen_model_input(
                 prompt=prompt, max_new_tokens=completion_request.max_tokens
             )
 
         # Llama 3.2 11B
-        prompt = None
-        images = None
-
-        for message in messages:
-            torchtune_contents = []
-            if isinstance(message["content"], list):
-                for content_dict in message["content"]:
-                    if content_dict["type"] == "text":
-                        assert (
-                            prompt is None
-                        ), "At most one text prompt is supported for each request"
-                        prompt = content_dict["text"]
-                    elif content_dict["type"] == "image_url":
-                        assert (
-                            images is None
-                        ), "At most one image is supported at the moment"
-
-                        base64_decoded = base64.b64decode(
-                            content_dict["image_url"].split(";base64,")[1]
-                        )
-                        images = [Image.open(BytesIO(base64_decoded))]
-
-        assert prompt is not None, "Text prompt must be specified in the request"
-
-        return self._gen_model_input(prompt, images, completion_request.max_tokens)
+
+        prompt = [
+            {"role": message["role"], "content": message["content"]}
+            for message in messages
+        ]
+
+        return self._gen_model_input(
+            prompt=prompt, max_new_tokens=completion_request.max_tokens
+        )
 
     def chunked_completion(self, completion_request: CompletionRequest):
         """Handle a chat completion request and yield a chunked response.