SAM2 Fast AMG: memory profiling and more compile (pytorch#1296)

Author: cpuhrsch

examples/sam2_amg_server/README.md

@@ -24,11 +24,11 @@ Experiments run on H100 and with batch size 1
 | mode            | mIoU               | mask count mismatch | avg. ms per request | max. memory (MiB (%)) | batch size | points per batch |
 | --------------  | -----------------  | ------------------- | ------------------- | --------------------- | ---------- | ---------------- |
 |        baseline | 1.0                |   0                 | 863                 |  4013MiB (4%)         |  1         |   64             |
-|              ao | 0.9999980926513672 |   6                 | 586                 |                       |  1         |   64             |
-|            fast | 0.9937329888343811 | 191                 | 333                 |                       |  1         | 1024             |
-|            fast | 0.9937219619750977 | 192                 | 324                 |                       | 16         | 1024             |
-|  fast + furious | 0.9804400205612183 | 292                 | 131                 |                       |  1         | 1024             |
-|  fast + furious | 0.9806423187255859 | 282                 | 130                 |                       | 16         | 1024             |
+|              ao | 0.9999980926513672 |   6                 | 586                 |  3257MiB (3%)         |  1         |   64             |
+|            fast | 0.993732988834381  | 191                 | 326                 | 27197MiB (27%)        |  1         | 1024             |
+|            fast | 0.9937511086463928 | 194                 | 315                 | 27488MiB (28%)        | 16         | 1024             |
+|  fast + furious | 0.9817246198654175 | 266                 | 120                 | 13616MiB (13%)        |  1         | 1024             |
+|  fast + furious | 0.9794579744338989 | 274                 | 122                 | 13808MiB (14%)        | 16         | 1024             |
 
 mask count mismatch counts the number of requests where the number of masks differ from the baseline.
 For example, the baseline may have chosen to segment an image into 18 masks, but the fast variant produces 17 or 19.

examples/sam2_amg_server/server.py

@@ -149,6 +149,26 @@ def profiler_runner(path, fn, *args, **kwargs):
     return result
 
 
+def memory_runner(path, fn, *args, **kwargs):
+    print("Start memory recording")
+    torch.cuda.synchronize()
+    torch.cuda.memory._record_memory_history(
+        True,
+        trace_alloc_max_entries=100000,
+        trace_alloc_record_context=True
+    )
+    result = fn(*args, **kwargs)
+    torch.cuda.synchronize()
+    snapshot = torch.cuda.memory._snapshot()
+    print("Finish memory recording")
+    import pickle
+    with open(path, 'wb') as f:
+        pickle.dump(snapshot, f)
+    # Use to convert pickle file into html
+    # python torch/cuda/_memory_viz.py trace_plot <snapshot>.pickle -o <snapshot>.html
+    return result
+
+
 def image_tensor_to_masks(example_image, mask_generator):
     masks = mask_generator.generate(example_image)
     return masks
@@ -187,7 +207,7 @@ def process_batch(batch, mask_generator):
         print(f"Processing batch of len {len(batch)} using generate_batch")
         masks = mask_generator.generate_batch(image_tensors)
     print(f"Took avg. {(time.time() - t) / len(batch)}s per batch entry")
-    # max_memory_allocated()
+    max_memory_allocated()
     return masks
 
 
@@ -259,6 +279,7 @@ def main(checkpoint_path,
          unittest=False,
          benchmark=False,
          profile=None,
+         memory_profile=None,
          verbose=False,
          points_per_batch=64,
          port=5000,
@@ -305,13 +326,6 @@ def main(checkpoint_path,
             dynamic=False,
         )
 
-        mask_generator.predictor.model.sam_prompt_encoder.forward = torch.compile(
-            mask_generator.predictor.model.sam_prompt_encoder.forward,
-            mode="max-autotune",
-            fullgraph=True,
-            dynamic=False,
-        )
-
         mask_generator.predictor._predict_masks = torch.compile(
             mask_generator.predictor._predict_masks,
             mode="max-autotune",
@@ -329,6 +343,7 @@ def main(checkpoint_path,
         mask_generator.predictor.model.image_encoder = mask_generator.predictor.model.image_encoder.to(torch.float16)
         # NOTE: Not baseline feature
         mask_generator.predictor._image_dtype = torch.float16
+        mask_generator.predictor._transforms_device = mask_generator.predictor.device
         torch.set_float32_matmul_precision('high')
         mask_generator.predictor.model.sam_mask_decoder = mask_generator.predictor.model.sam_mask_decoder.to(torch.float16)
         # NOTE: Not baseline feature
@@ -363,11 +378,15 @@ def main(checkpoint_path,
         for i, shapes in enumerate([example_shapes(), example_shapes_2()]):
             print(f"batch size {batch_size} example shapes {i} benchmark")
             random_images = [np.random.randint(0, 256, size=size, dtype=np.uint8) for size in shapes]
+            if batch_size > len(random_images):
+                num_repeat = (len(random_images) + batch_size) // batch_size
+                random_images = num_repeat * random_images
 
             if batch_size == 1:
                 [benchmark_fn(image_tensor_to_masks, r, mask_generator) for r in random_images]
             else:
                 random_images = random_images[:batch_size]
+                print("len(random_images): ", len(random_images))
                 benchmark_fn(image_tensors_to_masks, random_images, mask_generator)
 
     if profile is not None:
@@ -377,6 +396,13 @@ def main(checkpoint_path,
         else:
             profiler_runner(profile, image_tensors_to_masks, [image_tensor] * batch_size, mask_generator)
 
+    if memory_profile is not None:
+        print(f"Saving memory profile under {memory_profile}")
+        if batch_size == 1:
+            memory_runner(memory_profile, image_tensor_to_masks, image_tensor, mask_generator)
+        else:
+            memory_runner(memory_profile, image_tensors_to_masks, [image_tensor] * batch_size, mask_generator)
+
     if dry:
         return
 

torchao/_models/sam2/automatic_mask_generator.py

@@ -381,7 +381,6 @@ def _process_crop_batch(
         i = 0
         batch_features = self.predictor._features
         all_crop_data = []
-        all_all_batch_iterator_data = []
         for (cropped_im, crop_box, layer_idx, orig_size) in zip(all_cropped_im, all_crop_box, all_layer_idx, all_orig_size):
             cropped_im_size = cropped_im.shape[:2]
             self.predictor.reset_predictor()
@@ -425,9 +424,6 @@ def _process_crop_batch(
                     data = self._process_batch_fullgraph(points, im_size, crop_box, crop_box_torch, orig_size, normalize, orig_box_torch)
                     all_batch_iterator_data.append(data)
                 self.predictor.reset_predictor()
-            all_all_batch_iterator_data.append(all_batch_iterator_data)
-
-        for all_batch_iterator_data in all_all_batch_iterator_data:
 
             result_data = None
             with torch.autograd.profiler.record_function("all mask_to_rle_pytorch_2"):

torchao/_models/sam2/modeling/sam/mask_decoder.py

@@ -219,6 +219,10 @@ def predict_masks(
             # TODO: Not specifying scale kwarg in SDPA will cause NaN here
             # print("hs.isnan().any(): ", hs.isnan().any().item())
 
+        # TODO: These outputs are being immediately indexed.
+        # Is there something to remove?
+        # TODO: The fact that there's a crop box and we try to find stuff at the
+        # boundary later and there's generally cropping going on smells of padding.
         iou_token_out = hs[:, s, :]
         mask_tokens_out = hs[:, s + 1: (s + 1 + self.num_mask_tokens), :]
 

torchao/_models/sam2/sam2_image_predictor.py

@@ -66,6 +66,7 @@ def __init__(
         ]
 
         self._image_dtype = torch.float32
+        self._transforms_device = "cpu"
 
     @classmethod
     def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2ImagePredictor":
@@ -110,10 +111,10 @@ def set_image(
             raise NotImplementedError("Image format not supported")
 
         input_image = self._transforms.to_tensor(image)
-        # TODO: Doing these transforms on the GPU changes the numerics
+        # NOTE: Doing these transforms on the GPU changes the numerics
+        input_image = input_image.to(device=self._transforms_device)
         input_image = self._transforms.transforms(input_image)
         input_image = input_image.to(device=self.device)
-        # TODO: Doing this here instead causes masks to not match reference exactly
         # input_image = self._transforms.transforms(input_image)
         input_image = input_image[None, ...].to(dtype=self._image_dtype)
 
@@ -167,8 +168,10 @@ def set_image_batch(
             len(img_batch.shape) == 4 and img_batch.shape[1] == 3
         ), f"img_batch must be of size Bx3xHxW, got {img_batch.shape}"
         logging.info("Computing image embeddings for the provided images...")
-        backbone_out = self.model.forward_image(img_batch)
-        _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)
+        with torch.autograd.profiler.record_function("forward_image"):
+            backbone_out = self.model.forward_image(img_batch)
+        with torch.autograd.profiler.record_function("_prepare_backbone_features"):
+            _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)
         # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos
         if self.model.directly_add_no_mem_embed:
             vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
@@ -462,11 +465,11 @@ def _predict_masks_postprocess(self, low_res_masks, img_idx, return_logits, chan
             # Upscale the masks to the original image resolution
             if channel_1:
                 masks = self._transforms.postprocess_masks_1_channel(
-                    low_res_masks, self._orig_hw[img_idx]
+                    low_res_masks, self._orig_hw[img_idx], self._image_dtype
                 )
             else:
                 masks = self._transforms.postprocess_masks(
-                    low_res_masks, self._orig_hw[img_idx]
+                    low_res_masks, self._orig_hw[img_idx], self._image_dtype
                 )
         low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)
         if not return_logits:

torchao/_models/sam2/utils/amg.py

@@ -215,7 +215,8 @@ def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
     return mask.transpose()  # Put in C order
 
 
-def _mask_to_rle_pytorch_2_0(tensor: torch.Tensor) -> (torch.Tensor, torch.Tensor, torch.Tensor):
+@torch.compile(fullgraph=True, dynamic=True)
+def _mask_to_rle_pytorch_2_0_0(tensor: torch.Tensor) -> (torch.Tensor, torch.Tensor):
     """
     Encodes masks to an uncompressed RLE, in the format expected by
     pycoco tools.
@@ -227,33 +228,53 @@ def _mask_to_rle_pytorch_2_0(tensor: torch.Tensor) -> (torch.Tensor, torch.Tenso
     with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: change indices"):
         # Compute change indices
         diff = tensor[:, 1:] ^ tensor[:, :-1]
-        a = torch.tensor([[True]])
-        if diff.is_cuda:
-            a = a.pin_memory().cuda()
-            # a = a.to(diff.device)
+        # a = torch.tensor([[True]])
+        a = torch.ones((1, 1), dtype=bool, device=diff.device)
+        # if diff.is_cuda:
+        #     a = a.pin_memory().cuda()
+        #     # a = a.to(diff.device)
         a = a.expand_as(diff.narrow(1, 0, 1))
         diff = torch.cat([a, diff, a], dim=1)
-        if diff.numel() > 2147483646:
-            num_chunks = (diff.numel() + 2147483646) // 2147483646
-            change_indices = torch.cat([d.nonzero() for d in diff.chunk(num_chunks)])
-        else:
-            change_indices = diff.nonzero()
+    return diff
+
+
+@torch.compile(fullgraph=True, dynamic=True)
+def _mask_to_rle_pytorch_2_0_1(tensor: torch.Tensor, diff: torch.Tensor, change_indices: torch.Tensor) -> (torch.Tensor, torch.Tensor):
+    tensor = tensor.permute(0, 2, 1).flatten(1)
 
     with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: all_btw_idx"):
         alt_lens = diff.sum(dim=1)
 
         all_cur_idx = change_indices[:, 1]
-        all_btw_idx = torch.cat([all_cur_idx[1:], all_cur_idx[:1]]) - all_cur_idx
+        all_cur_idx_0 = all_cur_idx.narrow(0, 1, all_cur_idx.size(0) - 1)
+        all_cur_idx_1 = all_cur_idx.narrow(0, 0, 1)
+        all_btw_idx = torch.cat([all_cur_idx_0, all_cur_idx_1])
+        all_btw_idx = all_btw_idx - all_cur_idx
 
     with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: Encode run length"):
         alt_lens_nt = torch.nested.nested_tensor_from_jagged(all_btw_idx, lengths=alt_lens)
         # Encode run length
         counts_init = (tensor[:, 0] == 0)
-        return RLEData(alt_lens_nt=alt_lens_nt,
-                       counts_init=counts_init,
-                       b=b,
-                       h=h,
-                       w=w)
+    return alt_lens_nt, counts_init
+
+
+def _mask_to_rle_pytorch_2_0(tensor: torch.Tensor) -> RLEData:
+    b, h, w = tensor.shape
+    with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: _mask_to_rle_pytorch_2_0_0"):
+        diff = _mask_to_rle_pytorch_2_0_0(tensor)
+    with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: nonzero"):
+        if diff.numel() > 2147483646:
+            num_chunks = (diff.numel() + 2147483646) // 2147483646
+            change_indices = torch.cat([d.nonzero() for d in diff.chunk(num_chunks)])
+        else:
+            change_indices = diff.nonzero()
+    with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2: _mask_to_rle_pytorch_2_0_1"):
+        alt_lens_nt, counts_init = _mask_to_rle_pytorch_2_0_1(tensor, diff, change_indices)
+    return RLEData(alt_lens_nt=alt_lens_nt,
+                   counts_init=counts_init,
+                   b=b,
+                   h=h,
+                   w=w)
 
 
 def _mask_to_rle_pytorch_2_1(rle_data: RLEData):
@@ -276,7 +297,8 @@ def _mask_to_rle_pytorch_2_1(rle_data: RLEData):
 
 
 def mask_to_rle_pytorch_2(tensor: torch.Tensor) -> List[Dict[str, Any]]:
-    return _mask_to_rle_pytorch_2_1(_mask_to_rle_pytorch_2_0(tensor))
+    with torch.autograd.profiler.record_function("mask_to_rle_pytorch_2"):
+        return _mask_to_rle_pytorch_2_1(_mask_to_rle_pytorch_2_0(tensor))
 
 
 def area_from_rle(rle: Dict[str, Any]) -> int:

torchao/_models/sam2/utils/transforms.py

@@ -73,7 +73,7 @@ def transform_boxes(
         boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)
         return boxes
 
-    def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:
+    def postprocess_masks(self, masks: torch.Tensor, orig_hw, output_dtype) -> torch.Tensor:
         """
         Perform PostProcessing on output masks.
         """
@@ -114,10 +114,11 @@ def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:
             )
             masks = input_masks
 
+        masks = masks.to(output_dtype)
         masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False)
         return masks
 
-    def postprocess_masks_1_channel(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:
+    def postprocess_masks_1_channel(self, masks: torch.Tensor, orig_hw, output_dtype) -> torch.Tensor:
         """
         Perform PostProcessing on output masks.
         """
@@ -161,5 +162,6 @@ def postprocess_masks_1_channel(self, masks: torch.Tensor, orig_hw) -> torch.Ten
             )
             masks = input_masks
 
+        masks = masks.to(output_dtype)
         masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False)
         return masks