Upload FAI-Bench task files batch 51

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/README.md

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+# LLAMA
+
+## Eval on Llama 3.1 8B and Llama 3.2 3B
+
+We use lm-eval tasks for evaluating TorchAO Quantization APIs on HuggingFace models. The results are in the table below:
+
+| Model Name | Quantization Technique    | Acc   |Acc Norm| Word perplexity| Model Size (GB)   |
+|------------|---------------------------|-------|--------|----------------|-------------------|
+| Llama 3.1 8B  | None                   | 60.01 | 78.84  |       7.33     | 15.01             |
+| Llama 3.1 8B  | int4wo-128             | 58.10 | 77.06  |       8.25     | 4.76              |
+| Llama 3.1 8B  | int8wo                 | 59.92 | 78.95  |       7.34     | 8.04              |
+| Llama 3.1 8B  | int8dq                 | 60.01 | 78.82  |       7.45     | 8.03              |
+| Llama 3.1 8B  | float8wo               | 59.83 | 78.61  |       7.37     | 8.03              |
+| Llama 3.1 8B  | float8dq (PerRow)      | 59.86 | 78.57  |       7.41     | 8.04              |
+| Llama 3.1 8B  | float8dq (PerTensor)   | 59.95 | 78.66  |       7.42     | 8.03              |
+| Llama 3.1 8B  | gemlite (gp=128)       | 58.48 | 77.34  |       8.07     | 4.76              |
+
+| Model Name | Quantization Technique    | Acc   |Acc Norm| Word perplexity| Model Size (GB)   |
+|------------|---------------------------|-------|--------|----------------|-------------------|
+| Llama 3.2 3B  | None                   | 55.27 | 73.70  |       9.26     | 6.43              |
+| Llama 3.2 3B  | int4wo-128             | 53.13 | 71.31  |       10.36    | 2.29              |
+| Llama 3.2 3B  | int8wo                 | 55.15 | 73.44  |       9.28     | 3.61              |
+| Llama 3.2 3B  | int8dq                 | 55.00 | 73.29  |       9.43     | 3.61              |
+| Llama 3.2 3B  | float8wo               | 55.18 | 73.58  |       9.31     | 3.61              |
+| Llama 3.2 3B  | float8dq (PerRow)      | 55.18 | 73.37  |       9.33     | 3.61              |
+| Llama 3.2 3B  | float8dq (PerTensor)   | 55.16 | 73.53  |       9.35     | 3.61              |
+| Llama 3.2 3B  | gemlite (gp=128)       | 53.71 | 71.99  |      10.05     | 2.29              |
+
+To generate the above results run:
+```
+sh benchmarks/_models/eval_hf_models.sh
+```
+
+To run lm-eval for a different hf-model with AO quantization technique, run:
+```
+python benchmarks/_models/eval_hf_models.py --model_id meta-llama/Llama-3.1-8B --quantization float8dq-row --tasks wikitext hellaswag
+```
+Replace model id, quantization and tasks with your desired values Please refer to ([HuggingFace <-> TorchAO](https://huggingface.co/docs/transformers/main/en//quantization/torchao)) integration docs for more details about the supported quantization techniques.
+
+# SAM2
+sam2 is a fork of https://github.com/facebookresearch/sam2 at commit c2ec8e14a185632b0a5d8b161928ceb50197eddc
+
+It includes
+- modifications to enable fullgraph=True compile
+- `mask_to_rle_pytorch_2`
+- small performance changes and fixes
+- integration into torchao's packaging

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/_eval.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import lm_eval
+import torch
+import torch.nn.functional as F
+
+from torchao.quantization.GPTQ import MultiTensorInputRecorder
+
+try:  # lm_eval version 0.4
+    from lm_eval.evaluator import evaluate  # pyre-ignore[21]
+    from lm_eval.models.huggingface import HFLM as eval_wrapper  # pyre-ignore[21]
+    from lm_eval.tasks import get_task_dict  # pyre-ignore[21]
+except:  # lm_eval version 0.3
+    from lm_eval import base, evaluator, tasks
+
+    eval_wrapper = base.BaseLM
+    get_task_dict = tasks.get_task_dict
+    evaluate = evaluator.evaluate
+
+
+class TransformerEvalWrapper(eval_wrapper):
+    """
+    A wrapper class for GPTFast, providing integration with the lm-evaluation-harness library.
+    """
+
+    def __init__(
+        self, model, tokenizer, max_seq_length, input_prep_func=None, device="cuda"
+    ):
+        try:
+            super().__init__(device=device)
+        except TypeError:
+            # lm_eval 0.4.2 removed the default init
+            super().__init__("gpt2", device="cpu")
+
+        self._model = model
+        self.tokenizer = tokenizer
+        self._device = torch.device(device)
+        self._max_seq_length = max_seq_length
+
+        # need to take inps and convert to corrent input
+        # for model
+        self.input_prep_func = (
+            input_prep_func if input_prep_func is not None else lambda x: (x,)
+        )
+
+    def _model_call(self, inps):
+        # TODO: make batches work
+        input = self.input_prep_func(inps)
+
+        max_seq_length = min(max(inps.size()), self.max_length)
+        with torch.device(self._device):
+            self._model.setup_caches(self.batch_size, max_seq_length)
+        logits = self._model(*input)
+        return logits
+
+    def run_eval(self, tasks, limit):
+        try:
+            lm_eval.tasks.initialize_tasks()
+        except:
+            pass
+
+        task_dict = get_task_dict(tasks)
+        print("Evaluating Model On: ", task_dict)
+        with torch.no_grad():
+            result = evaluate(
+                self,
+                task_dict,
+                limit=limit,
+            )
+        for task, res in result["results"].items():
+            print(f"{task}: {res}")
+        return result
+
+    @property
+    def eot_token_id(self):
+        try:
+            return self.tokenizer.eos_id()
+        except:
+            return self.tokenizer.eos_id
+
+    @property
+    def max_length(self):
+        return self._max_seq_length
+
+    @property
+    def max_gen_toks(self):
+        return 50
+
+    @property
+    def batch_size(self):
+        return 1
+
+    @property
+    def device(self):
+        return self._device
+
+    def tok_decode(self, tokens):
+        decoded = self.tokenizer.decode(tokens)
+        return decoded
+
+    def tok_encode(self, string: str, **kwargs):
+        tokens = self.tokenizer.encode(string)
+        if hasattr(self.tokenizer, "bos_id"):
+            try:
+                tokens = [self.tokenizer.bos_id()] + tokens
+            except:
+                tokens = [self.tokenizer.bos_id] + tokens
+        return tokens
+
+    def _model_generate(self, context, max_length, eos_token_id):
+        raise Exception("unimplemented")
+
+
+class LMEvalInputRecorder(TransformerEvalWrapper):
+    def __init__(
+        self,
+        tokenizer,
+        calibration_seq_length,
+        input_prep_func=None,
+        vocab_size=32000,
+        pad_calibration_inputs=False,
+        pad_token=0,
+        device="cpu",
+        base_input_recorder_class=MultiTensorInputRecorder,
+    ):
+        super().__init__(
+            model=None,
+            tokenizer=tokenizer,
+            max_seq_length=calibration_seq_length,
+            input_prep_func=input_prep_func,
+            device=device,
+        )
+        self.vocab_size = vocab_size
+        self.calibration_seq_length = calibration_seq_length
+
+        self.pad_calibration_inputs = pad_calibration_inputs
+        self.pad_token = pad_token
+
+        # Initialize inputs as a list of two empty lists for input tensors and indices
+        self.base_input_recorder = base_input_recorder_class()
+
+    def record_inputs(self, calibration_tasks, calibration_limit):
+        try:
+            lm_eval.tasks.initialize_tasks()
+        except:
+            pass
+
+        task_dict = get_task_dict(calibration_tasks)
+        print("Obtaining GPTQ calibration inputs on: ", calibration_tasks)
+
+        evaluate(
+            self,
+            task_dict,
+            limit=calibration_limit,
+        )
+        return self
+
+    def get_inputs(self):  # for BC
+        return self.get_recorded_inputs()
+
+    def get_recorded_inputs(self):
+        return self.base_input_recorder.get_recorded_inputs()
+
+    def get_recorded_args_and_kwargs(self):
+        return self.base_input_recorder.get_recorded_args_and_kwargs()
+
+    def _model_call(self, inps):
+        inps = inps.squeeze(0)
+        T = len(inps)
+        if (
+            # Can't use inputs that are too short when padding is disabled
+            (T < self.calibration_seq_length and not self.pad_calibration_inputs)
+            or
+            # Can't use inputs that actually use the token we use for padding
+            (self.pad_calibration_inputs and self.pad_token in inps)
+        ):
+            # Give random output
+            return torch.randn(
+                (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device
+            )
+
+        # Pad or truncate to the correct size
+        if T >= self.calibration_seq_length:
+            inps = inps[: self.calibration_seq_length]
+        else:
+            inps = F.pad(
+                inps, (0, self.calibration_seq_length - T), value=self.pad_token
+            )
+
+        inps = inps.unsqueeze(0)
+        model_in = self.input_prep_func(inps)
+
+        self.base_input_recorder(*model_in)
+
+        # Output `something` with the correct shape to keep eval going
+        return torch.randn(
+            (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device
+        )
+
+
+InputRecorder = LMEvalInputRecorder  # for BC

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/README.md

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+## Mixtral-MoE
+
+This folder contains code and scripts for benchmarking the Mixtral-MoE model.
+Running
+
+`sh scripts/prepare.sh`
+
+should download the model and `sh run.sh` will run teh benchmarks.

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/generate.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import itertools
+import sys
+import time
+from pathlib import Path
+from typing import Optional, Tuple
+
+import torch
+import torch._dynamo.config
+import torch._inductor.config
+
+from torchao.utils import get_model_size_in_bytes
+
+torch.manual_seed(0)
+
+
+def device_sync(device):
+    if "cuda" in device:
+        torch.cuda.synchronize(device)
+    elif "cpu" in device:
+        pass
+    else:
+        print(f"device={device} is not yet suppported")
+
+
+torch._inductor.config.coordinate_descent_tuning = True
+torch._inductor.config.triton.unique_kernel_names = True
+torch._inductor.config.fx_graph_cache = True  # Experimental feature to reduce compilation times, will be on by default in future
+torch._dynamo.config.capture_scalar_outputs = True
+
+# support running without installing as a package
+wd = Path(__file__).parent.parent.resolve()
+sys.path.append(str(wd))
+
+from model import Transformer
+from sentencepiece import SentencePieceProcessor
+
+
+def multinomial_sample_one_no_sync(
+    probs_sort,
+):  # Does multinomial sampling without a cuda synchronization
+    q = torch.empty_like(probs_sort).exponential_(1)
+    return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+
+def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None):
+    logits = logits / max(temperature, 1e-5)
+
+    if top_k is not None:
+        v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+        pivot = v.select(-1, -1).unsqueeze(-1)
+        logits = torch.where(logits < pivot, -float("Inf"), logits)
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    return probs
+
+
+def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
+    probs = logits_to_probs(logits[:, -1], temperature, top_k)
+    idx_next = multinomial_sample_one_no_sync(probs)
+    return idx_next, probs
+
+
+def prefill(
+    model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs
+) -> torch.Tensor:
+    # input_pos: [B, S]
+    logits = model(x, input_pos)
+    return sample(logits, **sampling_kwargs)[0]
+
+
+def decode_one_token(
+    model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    # input_pos: [B, 1]
+    assert input_pos.shape[-1] == 1
+    logits = model(x, input_pos)
+    return sample(logits, **sampling_kwargs)
+
+
+def decode_n_tokens(
+    model: Transformer,
+    cur_token: torch.Tensor,
+    input_pos: torch.Tensor,
+    num_new_tokens: int,
+    callback=lambda _: _,
+    **sampling_kwargs,
+):
+    new_tokens, new_probs = [], []
+    for i in range(num_new_tokens):
+        with torch.backends.cuda.sdp_kernel(
+            enable_flash=False, enable_mem_efficient=False, enable_math=True
+        ):  # Actually better for Inductor to codegen attention here
+            next_token, next_prob = decode_one_token(
+                model, cur_token, input_pos, **sampling_kwargs
+            )
+            next_token, next_prob = next_token.clone(), next_prob.clone()
+
+            input_pos += 1
+            new_tokens.append(next_token.clone())
+            callback(new_tokens[-1])
+            new_probs.append(next_prob.clone())
+            cur_token = next_token
+
+    return new_tokens, new_probs
+
+
+def model_forward(model, x, input_pos):
+    return model(x, input_pos)
+
+
+@torch.no_grad()
+def generate(
+    model: Transformer,
+    prompt: torch.Tensor,
+    max_new_tokens: int,
+    batch_size: int,
+    *,
+    interactive: bool,
+    callback=lambda x: x,
+    **sampling_kwargs,
+) -> torch.Tensor:
+    """
+    Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
+    """
+    device, _ = prompt.device, prompt.dtype
+
+    T = prompt.size(-1)
+    max_seq_length = (
+        min(T + max_new_tokens, model.config.block_size) if not interactive else 350
+    )
+    new_tokens = max_seq_length - T
+
+    # duplicate prompt for batch_size
+    prompt = prompt.repeat(batch_size, 1)
+
+    # create an empty tensor of the expected final shape and fill in the current tokens
+    seq = torch.empty(batch_size, max_seq_length, dtype=prompt.dtype, device=device)
+    seq[:, :T] = prompt
+
+    with torch.device(device):
+        model.setup_caches(max_batch_size=batch_size, max_seq_length=max_seq_length)
+
+    input_pos = torch.arange(0, T, device=device)
+    next_token = prefill(
+        model, prompt.view(batch_size, -1), input_pos, **sampling_kwargs
+    )
+    seq[:, T] = next_token.squeeze()
+
+    input_pos = torch.tensor([T], device=device, dtype=torch.int)
+    generated_tokens, _ = decode_n_tokens(
+        model,
+        next_token.view(batch_size, -1),
+        input_pos,
+        new_tokens - 1,
+        callback=callback,
+        **sampling_kwargs,
+    )
+    seq = torch.cat((seq[:, : T + 1], *generated_tokens), dim=-1)
+
+    return seq
+
+
+def encode_tokens(tokenizer, string, bos=True, device="cuda"):
+    tokens = tokenizer.encode(string)
+    if bos:
+        tokens = [tokenizer.bos_id()] + tokens
+    return torch.tensor(tokens, dtype=torch.int, device=device)
+
+
+def _load_model(checkpoint_path, device, precision):
+    with torch.device("meta"):
+        model = Transformer.from_name(checkpoint_path.parent.name)
+
+    try:
+        checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True)
+        model.load_state_dict(checkpoint, assign=True)
+    except:
+        model = Transformer.from_name(checkpoint_path.parent.name)
+
+    model = model.to(device=device, dtype=precision)
+    return model.eval()
+
+
+B_INST, E_INST = "[INST]", "[/INST]"
+
+
+def main(
+    prompt: str = "Hello, my name is",
+    interactive: bool = False,
+    num_samples: int = 5,
+    max_new_tokens: int = 100,
+    batch_size: int = 1,
+    top_k: int = 200,
+    temperature: float = 0.8,
+    checkpoint_path: Path = Path("checkpoints/mistralai/Mixtral-8x7B-v0.1/model.pth"),
+    compile: bool = True,
+    compile_prefill: bool = False,
+    moe_quant: Optional[str] = None,
+    profile: Optional[Path] = None,
+    memory_profile: Optional[Path] = None,
+    device="cuda",
+) -> None:
+    """Generates text samples based on a pre-trained Transformer model and tokenizer."""
+    assert checkpoint_path.is_file(), checkpoint_path
+    tokenizer_path = checkpoint_path.parent / "tokenizer.model"
+    assert tokenizer_path.is_file(), str(tokenizer_path)
+    print(f"Using device={device}")
+    precision = torch.bfloat16
+    is_chat = "chat" in str(checkpoint_path)
+
+    if device == "cuda" and memory_profile is not None:
+        torch.cuda.memory._record_memory_history(
+            True, trace_alloc_max_entries=500000, trace_alloc_record_context=True
+        )
+
+    print("Loading model ...")
+    t0 = time.time()
+    model = _load_model(checkpoint_path, "cpu", precision)
+
+    print(f"Time to load model: {time.time() - t0:.02f} seconds")
+    t0 = time.time()
+
+    tokenizer = SentencePieceProcessor(model_file=str(tokenizer_path))
+    encoded = encode_tokens(tokenizer, prompt, bos=True, device=device)
+    prompt_length = encoded.size(0)
+
+    torch.manual_seed(1234)
+    model_size = sum(
+        [
+            p.numel() * p.dtype.itemsize
+            for p in itertools.chain(model.parameters(), model.buffers())
+        ]
+    )
+
+    from torchao.prototype.moe_quant.utils import (
+        MoEQuantConfig,
+        UseFakeExtraDimTensor,
+        cond_ffn_filter,
+    )
+    from torchao.quantization.quant_api import (
+        Float8DynamicActivationFloat8WeightConfig,
+        Float8WeightOnlyConfig,
+        Int4WeightOnlyConfig,
+        Int8DynamicActivationInt8WeightConfig,
+        Int8DynamicActivationIntxWeightConfig,
+        Int8WeightOnlyConfig,
+        PackedLinearInt8DynamicActivationIntxWeightLayout,
+        PerRow,
+        quantize_,
+    )
+
+    if moe_quant:
+        torch._dynamo.config.capture_dynamic_output_shape_ops = True
+        config = None
+        if "int8wo-base" in moe_quant:
+            config = MoEQuantConfig(Int8WeightOnlyConfig())
+
+        elif "int8wo" in moe_quant:
+            config = MoEQuantConfig(
+                Int8WeightOnlyConfig(),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+
+        elif "int8dq-base" in moe_quant:
+            config = MoEQuantConfig(Int8DynamicActivationInt8WeightConfig())
+
+        elif "int8dq" in moe_quant:
+            config = MoEQuantConfig(
+                Int8DynamicActivationInt8WeightConfig(),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+
+        elif "int4wo-base" in moe_quant:
+            config = MoEQuantConfig(Int4WeightOnlyConfig())
+
+        elif "int4wo" in moe_quant:
+            config = MoEQuantConfig(
+                Int4WeightOnlyConfig(),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+
+        elif "fp8wo-base" in moe_quant:
+            config = MoEQuantConfig(Float8WeightOnlyConfig())
+
+        elif "fp8wo" in moe_quant:
+            config = MoEQuantConfig(
+                Float8WeightOnlyConfig(),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+
+        elif "fp8dq-base" in moe_quant:
+            config = MoEQuantConfig(
+                Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+            )
+
+        elif "fp8dq" in moe_quant:
+            config = MoEQuantConfig(
+                Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+
+        elif "intxdq" in moe_quant:
+            config = MoEQuantConfig(
+                Int8DynamicActivationIntxWeightConfig(
+                    layout=PackedLinearInt8DynamicActivationIntxWeightLayout(),
+                ),
+                use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE,
+            )
+        else:
+            assert config is not None, (
+                f"expected moe_quant to match one of the options but got {moe_quant}"
+            )
+
+        if config is not None:
+            quantize_(model, config, filter_fn=cond_ffn_filter, device=device)
+            print(
+                f"Time to apply quantization with config {config} to model: {time.time() - t0:.02f} seconds"
+            )
+
+    model.to(device=device)
+    device_sync(device=device)
+
+    if compile:
+        # moe quant + compile causes repeated warnings
+        import warnings
+
+        warnings.simplefilter("ignore", lineno=84)
+        warnings.simplefilter("ignore", lineno=105)
+
+        torch._inductor.config.assert_indirect_indexing = False
+
+        global decode_one_token, prefill
+
+        if batch_size == 1 and (isinstance(moe_quant, str) and "base" in moe_quant):
+            decode_one_token = torch.compile(
+                decode_one_token, mode="reduce-overhead", fullgraph=True
+            )
+        else:
+            decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead")
+
+        if args.compile_prefill:
+            prefill = torch.compile(prefill, fullgraph=True, dynamic=True)
+
+    aggregate_metrics = {
+        "tokens_per_sec": [],
+    }
+    start = -1 if compile else 0
+
+    for i in range(start, num_samples):
+        device_sync(device=device)  # MKG
+        if i >= 0 and interactive:
+            prompt = input("What is your prompt? ")
+            if is_chat:
+                prompt = f"{B_INST} {prompt.strip()} {E_INST}"
+            encoded = encode_tokens(tokenizer, prompt, bos=True, device=device)
+
+        if interactive and i >= 0:
+            buffer = []
+            period_id = tokenizer.encode(".")[0]
+            done_generating = False
+
+            def callback(x):
+                nonlocal done_generating
+                if done_generating:
+                    return
+                buffer.append(tokenizer.decode([period_id] + x.tolist())[1:])
+                if x.item() == tokenizer.eos_id():
+                    done_generating = True
+                if len(buffer) == 4 or done_generating:
+                    print("".join(buffer), end="", flush=True)
+                    buffer.clear()
+                # print(, end='', flush=True)
+        else:
+            callback = lambda x: x
+        t0 = time.perf_counter()
+        import contextlib
+
+        if i != num_samples - 1 or not profile:
+            prof = contextlib.nullcontext()
+        else:
+            torch.profiler._utils._init_for_cuda_graphs()
+            prof = torch.profiler.profile()
+        with prof:
+            y = generate(
+                model,
+                encoded,
+                max_new_tokens,
+                batch_size,
+                interactive=interactive,
+                callback=callback,
+                temperature=temperature,
+                top_k=top_k,
+            )
+        if i == -1:
+            print(f"Compilation time: {time.perf_counter() - t0:.2f} seconds")
+            continue
+        if hasattr(prof, "export_chrome_trace"):
+            prof.export_chrome_trace(f"{profile}.json")
+        device_sync(device=device)  # MKG
+        t = time.perf_counter() - t0
+
+        if not interactive:
+            pass
+            print(tokenizer.decode(y[0].tolist()))
+        else:
+            print()
+        tokens_generated = y.size(-1) - prompt_length
+        tokens_sec = tokens_generated / t
+        aggregate_metrics["tokens_per_sec"].append(tokens_sec)
+        print(
+            f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_sec:.02f} tokens/sec"
+        )
+        print(f"Bandwidth achieved: {model_size * tokens_sec / 1e9:.02f} GB/s")
+
+        if i == 0 and device == "cuda" and memory_profile is not None:
+            snapshot = torch.cuda.memory._snapshot()
+            with open(f"{memory_profile}.pickle", "wb") as f:
+                from pickle import dump
+
+                dump(snapshot, f)
+            print(
+                f"\nmemory profile {memory_profile}.pickle saved, to convert that to a usable file, use",
+                "python pytorch/torch/cuda/_memory_viz.py trace_plot <pickle file> -o <desired output name>.html",
+            )
+            break
+
+    tokpersec = torch.mean(torch.tensor(aggregate_metrics["tokens_per_sec"])).item()
+    print(f"Average tokens/sec: {tokpersec:.2f}")
+    if batch_size > 1:
+        print(f"Average tokens/sec including batches {batch_size * tokpersec:.2f}")
+    print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB")
+    print(f"model size: {get_model_size_in_bytes(model) / 1e9:.02f}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Your CLI description.")
+
+    parser.add_argument(
+        "--prompt", type=str, default="Hello, my name is", help="Input prompt."
+    )
+    parser.add_argument(
+        "--interactive",
+        action="store_true",
+        help="Whether to launch in interactive mode",
+    )
+    parser.add_argument("--num_samples", type=int, default=5, help="Number of samples.")
+    parser.add_argument(
+        "--max_new_tokens", type=int, default=200, help="Maximum number of new tokens."
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=1, help="Batch size to benchmark with"
+    )
+    parser.add_argument("--top_k", type=int, default=200, help="Top-k for sampling.")
+    parser.add_argument(
+        "--temperature", type=float, default=0.8, help="Temperature for sampling."
+    )
+    parser.add_argument(
+        "--checkpoint_path",
+        type=Path,
+        default=Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf/model.pth"),
+        help="Model checkpoint path.",
+    )
+    parser.add_argument(
+        "--compile", action="store_true", help="Whether to compile the model."
+    )
+    parser.add_argument(
+        "--compile_prefill",
+        action="store_true",
+        help="Whether to compile the prefill (improves prefill perf, but higher compile times)",
+    )
+    # parser.add_argument('-q', '--quantization', type=str, help='Which quantization techniques to apply: int8dq, int8wo, int4wo, fp8')
+    parser.add_argument(
+        "--moe_quant",
+        type=str,
+        help="Which quantization techniques to apply: int8dq, int8wo, int4wo, fp8wo, fp8dq",
+    )
+    parser.add_argument("--profile", type=Path, default=None, help="Profile path.")
+    parser.add_argument(
+        "--memory_profile", type=Path, default=None, help="filename for memory profile."
+    )
+    parser.add_argument("--device", type=str, default="cuda", help="device to use")
+
+    args = parser.parse_args()
+    print(args)
+    main(
+        args.prompt,
+        args.interactive,
+        args.num_samples,
+        args.max_new_tokens,
+        args.batch_size,
+        args.top_k,
+        args.temperature,
+        args.checkpoint_path,
+        args.compile,
+        args.compile_prefill,
+        args.moe_quant,
+        args.profile,
+        args.memory_profile,
+        args.device,
+    )

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/model.py

@@ -0,0 +1,464 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+
+from torchao.prototype.moe_quant.utils import FakeExtraDimTensor
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [
+            config
+            for config in transformer_configs
+            if config in str(name).upper() or config in str(name)
+        ]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "Mixtral-8x7B-Instruct-v0.1": dict(
+        block_size=32768,
+        n_layer=32,
+        n_head=32,
+        n_local_heads=8,
+        dim=4096,
+        intermediate_size=14336,
+        rope_base=1000000.0,
+        num_experts=8,
+        num_activated_experts=2,
+    ),
+}
+
+
+class KVCache(nn.Module):
+    def __init__(
+        self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16
+    ):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(
+            TransformerBlock(config) for _ in range(config.n_layer)
+        )
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        self.freqs_cis: Optional[Tensor] = None
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        if (
+            self.max_seq_length >= max_seq_length
+            and self.max_batch_size >= max_batch_size
+        ):
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(
+                max_batch_size, max_seq_length, self.config.n_local_heads, head_dim
+            )
+
+        self.freqs_cis = precompute_freqs_cis(
+            self.config.block_size,
+            self.config.dim // self.config.n_head,
+            self.config.rope_base,
+        )
+        self.causal_mask = torch.tril(
+            torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool)
+        )
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str):
+        return cls(ModelArgs.from_name(name))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.attention = Attention(config)
+        self.block_sparse_moe = MOEFeedForwardAOQuantizable(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(
+        self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor
+    ) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(
+        self,
+        x: Tensor,
+        freqs_cis: Tensor,
+        mask: Tensor,
+        input_pos: Optional[Tensor] = None,
+    ) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+# class ConditionalFeedForward(nn.Module):
+#     def __init__(self, config):
+#         super().__init__()
+#         self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+#         self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
+#         self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+
+#     def forward(self, x: Tensor, expert_indices: Tensor) -> Tensor:
+#         w1_weights = self.w1[expert_indices] # [T, A, D, D]
+#         w3_weights = self.w3[expert_indices] # [T, A, D, D]
+#         w2_weights = self.w2[expert_indices]  # [T, A, D, D]
+#         x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights))
+#         x3 = torch.einsum('ti, taoi -> tao', x, w3_weights)
+#         expert_outs =  torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights)
+#         return expert_outs
+
+
+# class MOEFeedForward(nn.Module):
+#     def __init__(self, config) -> None:
+#         super().__init__()
+#         self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+#         self.cond_ffn = ConditionalFeedForward(config)
+#         self.dim = config.dim
+#         self.num_activated_experts = config.num_activated_experts
+#     def forward(self, x: Tensor) -> Tensor:
+#         x = x.view(-1, self.dim)
+#         # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+#         # x: [T, D]
+#         scores = self.gate(x) # [T, E]
+#         expert_weights = F.softmax(scores, dim=-1)
+#         expert_weights, expert_indices = torch.topk(expert_weights, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+#         expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+#         expert_outs = self.cond_ffn(x, expert_indices)
+#         return torch.einsum('tai,ta -> ti', expert_outs, expert_weights)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(seq_len: int, n_elem: int, base: int = 10000) -> Tensor:
+    freqs = 1.0 / (
+        base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem)
+    )
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
+
+
+# T tokens
+# E experts
+# D dim
+# I intermediate dim
+# A activated experts
+# T'(e) tokens for expert e
+
+
+class MOEFeedForwardAOQuantizable(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForwardAOQuantizable(config)
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+
+    def forward(self, x: Tensor) -> Tensor:
+        batch_size = x.shape[0]
+        x = x.view(-1, self.dim)  # x: [T, D]
+        scores = self.gate(x)  # [T, E]
+        expert_weights = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(
+            expert_weights, self.num_activated_experts, dim=-1
+        )  # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True).to(x.dtype)  # [T, A]
+        out = self.cond_ffn(
+            x, expert_indices, expert_weights, self.num_activated_experts
+        )
+        return out.reshape(batch_size, -1, self.dim)
+
+
+class ConditionalFeedForwardAOQuantizable(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.w1 = nn.Parameter(
+            torch.empty(config.num_experts, config.intermediate_size, config.dim)
+        )  # E, I, D
+        self.w2 = nn.Parameter(
+            torch.empty(config.num_experts, config.dim, config.intermediate_size)
+        )  # E, D, I
+        self.w3 = nn.Parameter(
+            torch.empty(config.num_experts, config.intermediate_size, config.dim)
+        )  # E, I, D
+        self.num_experts = config.num_experts
+
+    def forward(
+        self,
+        x: Tensor,  # T, D
+        expert_indices: Tensor,  # T, A
+        expert_weights: Tensor,  # T, A
+        num_activated_experts: int,
+    ) -> Tensor:
+        num_tokens, dim = x.shape
+        num_token_activations = num_tokens * num_activated_experts
+        if x.shape[0] == 1 and not isinstance(
+            self.w1, FakeExtraDimTensor
+        ):  # only 1 token (can be done without graph breaks when compiled)
+            outs = []
+            expert_indices = expert_indices.view(num_activated_experts)
+            # collect used experts
+            w1 = self.w1[expert_indices]
+            w2 = self.w2[expert_indices]
+            w3 = self.w3[expert_indices]
+
+            # run token through each expert
+            for index in range(num_activated_experts):
+                y1 = F.silu(F.linear(x, w1[index]))
+                y3 = F.linear(x, w3[index])
+                y2 = w2[index]
+                cur_out = F.linear(y1 * y3, y2)
+                outs.append(cur_out)
+
+            # combine outputs
+            final_out = (
+                (torch.cat(outs, dim=0) * expert_weights.view(-1, 1))
+                .sum(dim=0)
+                .unsqueeze(-1)
+            )
+            return final_out
+        else:
+            expert_list = [x for x in range(self.num_experts)]
+
+            # shuffle tokens into groups for each expert
+            ordered_token_activations = expert_indices.view(-1).argsort(
+                stable=True
+            )  # [A]
+            ordered_token_indices = (
+                ordered_token_activations.div(num_activated_experts)
+                .floor()
+                .to(torch.int64)
+            )  #  [T]
+
+            if not expert_indices.is_cuda:  # histc doesn't work on cpu for integers
+                num_tokens_per_expert = torch.bincount(
+                    expert_indices.view(-1) + 1, minlength=self.num_experts + 1
+                )
+            else:
+                num_tokens_per_expert = torch.histc(
+                    expert_indices,
+                    bins=self.num_experts + 1,
+                    min=-1,
+                    max=self.num_experts,
+                )  #  [E+1] (added leading 0 so can be used for indexing)
+            cum_tokens_per_expert = num_tokens_per_expert.cumsum(0).to(
+                torch.int64
+            )  #  [E+1]
+
+            @torch._dynamo.disable()
+            def group_tokens_by_expert(
+                ordered_token_indices, cum_tokens_per_expert, expert_list
+            ):
+                token_indices_per_expert = [
+                    ordered_token_indices[
+                        cum_tokens_per_expert[expert] : cum_tokens_per_expert[
+                            expert + 1
+                        ]
+                    ]
+                    for expert in expert_list
+                ]  # [T'(e1)], [T'(e2)] ...
+                return token_indices_per_expert
+
+            token_indices_per_expert = group_tokens_by_expert(
+                ordered_token_indices, cum_tokens_per_expert, expert_list
+            )
+            tokens_grouped_by_expert = [
+                x[indices] for indices in token_indices_per_expert
+            ]
+
+            # calculate outputs for each expert
+            outs = []
+            for cur_x, expert in zip(tokens_grouped_by_expert, expert_list):
+                w1 = self.w1[expert]  # I, D
+                w2 = self.w2[expert]  # D, I
+                w3 = self.w3[expert]  # I, D
+
+                cur_out = F.linear(
+                    F.silu(F.linear(cur_x, w1)) * F.linear(cur_x, w3), w2
+                )  # [T'(e), D]
+                outs.append(cur_out)
+
+            # weigh outputs
+            ordered_outs = torch.cat(outs, dim=0)  # [T*A, D]
+            ordered_token_activation_weights = expert_weights.view(-1, 1)[
+                ordered_token_activations
+            ].view(-1, 1)  # [T*A, 1]
+            weighted_ordered_outs = (
+                ordered_outs * ordered_token_activation_weights
+            )  # [T*A, D]
+
+            # sum weighted token-activation outputs together for each token
+            final_out = torch.zeros_like(x)  #  [T, D]
+            final_out = final_out.scatter_add(
+                dim=0,
+                index=ordered_token_indices.unsqueeze(-1)
+                .expand(num_token_activations, dim)
+                .to(torch.int64),
+                src=weighted_ordered_outs,
+            )
+        return final_out

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/run.sh

@@ -0,0 +1,39 @@
+export MODEL_REPO=mistralai/Mixtral-8x7B-Instruct-v0.1
+export CHECKPOINT_PATH=checkpoints/
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8wo --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8wo --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8wo-base --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8wo-base --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int4wo --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int4wo --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int4wo-base --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int4wo-base --compile
+
+# # EXPERT CHOICE
+# # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8dq --compile
+# # # # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8dq --compile
+# # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8dq-base --compile
+# # # # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8dq-base --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8wo --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8wo --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8wo-base --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8wo-base --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8dq --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8dq --compile
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8dq-base --compile
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8dq-base --compile
+
+# ARM
+# python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant intxdq --device cpu
+# python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant intxdq --compile --device cpu

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/scripts/convert_hf_checkpoint.py

@@ -0,0 +1,115 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import glob
+import re
+import sys
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+# support running without installing as a package
+wd = Path(__file__).parent.parent.resolve()
+sys.path.append(str(wd))
+
+from model import ModelArgs
+
+
+@torch.inference_mode()
+def convert_hf_checkpoint(
+    *,
+    checkpoint_dir: Path = Path("checkpoints/mistralai/Mixtral-8x7B-v0.1"),
+    model_name: Optional[str] = None,
+) -> None:
+    if model_name is None:
+        model_name = checkpoint_dir.name
+
+    config = ModelArgs.from_name(model_name)
+    print(f"Model config {config.__dict__}")
+
+    weight_map = {
+        "tok_embeddings.weight": "tok_embeddings.weight",
+        "layers.{}.attention.wq.weight": "layers.{}.attention.wq.weight",
+        "layers.{}.attention.wk.weight": "layers.{}.attention.wk.weight",
+        "layers.{}.attention.wv.weight": "layers.{}.attention.wv.weight",
+        "layers.{}.attention.wo.weight": "layers.{}.attention.wo.weight",
+        "layers.{}.block_sparse_moe.w1": "layers.{}.block_sparse_moe.cond_ffn.w1",
+        "layers.{}.block_sparse_moe.w2": "layers.{}.block_sparse_moe.cond_ffn.w2",
+        "layers.{}.block_sparse_moe.w3": "layers.{}.block_sparse_moe.cond_ffn.w3",
+        "layers.{}.block_sparse_moe.gate.weight": "layers.{}.block_sparse_moe.gate.weight",
+        "layers.{}.attention_norm.weight": "layers.{}.attention_norm.weight",
+        "layers.{}.ffn_norm.weight": "layers.{}.ffn_norm.weight",
+        "norm.weight": "norm.weight",
+        "output.weight": "output.weight",
+    }
+
+    pt_files = glob.glob(str(checkpoint_dir / "*.pt"))
+
+    merged_result = {}
+    for file in sorted(pt_files):
+        state_dict = torch.load(
+            str(file), map_location="cpu", mmap=True, weights_only=True
+        )
+        merged_result.update(state_dict)
+    final_result = {}
+    for key, value in merged_result.items():
+        if "layers" in key:
+            abstract_key = re.sub(r".(\d+).", ".{}.", key)
+            layer_num = re.search(r"\d+", key).group(0)
+            new_key = weight_map[abstract_key]
+            if new_key is None:
+                continue
+            new_key = new_key.format(layer_num)
+        else:
+            new_key = weight_map[key]
+
+        final_result[new_key] = value
+
+    for key in tuple(final_result.keys()):
+        if "wq" in key:
+            q = final_result[key]
+            k = final_result[key.replace("wq", "wk")]
+            v = final_result[key.replace("wq", "wv")]
+            final_result[key.replace("wq", "wqkv")] = torch.cat([q, k, v])
+            del final_result[key]
+            del final_result[key.replace("wq", "wk")]
+            del final_result[key.replace("wq", "wv")]
+        elif "w1" in key or "w3" in key:
+            final_result[key] = (
+                final_result[key]
+                .reshape(config.num_experts, config.intermediate_size, config.dim)
+                .contiguous()
+            )
+        elif "w2" in key:
+            final_result[key] = (
+                final_result[key]
+                .reshape(config.num_experts, config.intermediate_size, config.dim)
+                .permute(0, 2, 1)
+                .contiguous()
+            )
+        elif "gate" in key:
+            final_result[key] = final_result[key].contiguous()
+
+    print(f"Saving checkpoint to {checkpoint_dir / 'model.pth'}")
+    torch.save(final_result, checkpoint_dir / "model.pth")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Convert HuggingFace checkpoint.")
+    parser.add_argument(
+        "--checkpoint_dir",
+        type=Path,
+        default=Path("checkpoints/mistralai/Mixtral-8x7B-v0.1"),
+    )
+    parser.add_argument("--model_name", type=str, default=None)
+
+    args = parser.parse_args()
+    convert_hf_checkpoint(
+        checkpoint_dir=args.checkpoint_dir,
+        model_name=args.model_name,
+    )

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/scripts/download.py

@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+from typing import Optional
+
+from requests.exceptions import HTTPError
+
+
+def hf_download(repo_id: Optional[str] = None, hf_token: Optional[str] = None) -> None:
+    from huggingface_hub import snapshot_download
+
+    os.makedirs(f"checkpoints/{repo_id}", exist_ok=True)
+    try:
+        snapshot_download(
+            repo_id,
+            local_dir=f"checkpoints/{repo_id}",
+            local_dir_use_symlinks=False,
+            token=hf_token,
+            ignore_patterns="*.safetensors",
+        )
+    except HTTPError as e:
+        if e.response.status_code == 401:
+            print(
+                "You need to pass a valid `--hf_token=...` to download private checkpoints."
+            )
+        else:
+            raise e
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Download data from HuggingFace Hub.")
+    parser.add_argument(
+        "--repo_id",
+        type=str,
+        default="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        help="Repository ID to download from.",
+    )
+    parser.add_argument(
+        "--hf_token", type=str, default=None, help="HuggingFace API token."
+    )
+
+    args = parser.parse_args()
+    hf_download(args.repo_id, args.hf_token)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/mixtral-moe/scripts/prepare.sh

@@ -0,0 +1,2 @@
+python scripts/download.py --repo_id mistralai/Mixtral-8x7B-Instruct-v0.1
+python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/mistralai/Mixtral-8x7B-Instruct-v0.1

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/.gitignore

@@ -0,0 +1,3 @@
+tmp
+checkpoints
+datasets

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/README.md

@@ -0,0 +1,23 @@
+# benchmarking instructions:
+
+Setup your enviornment with:
+```
+conda env create -n "saf-ao" python=3.10
+conda activate saf-ao
+pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126
+pip3 install git+https://github.com/pytorch-labs/segment-anything-fast.git
+pip3 install tqdm fire pandas
+cd ../.. && python setup.py install
+```
+
+Then download data and models by running
+```
+sh setup.sh
+```
+
+Finally, you can run benchmarks with
+```
+sh benchmark.sh
+```
+
+You can check out the result in results.csv

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/benchmark.sh

@@ -0,0 +1,17 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+# baseline
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half bfloat16 --device cuda --print_header True
+# int8 dynamic quant (all)
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half bfloat16 --device cuda --compress int8_dynamic_quant
+# 2:4 sparsity (all)
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half bfloat16 --device cuda --compress sparse_mlp_only
+# 2:4 sparsity (mlp only)
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half bfloat16 --device cuda --compress sparse
+# int8 dynamic quant + 2:4 sparsity (attn: int8, mlp lin1: int8+2:4 fuse mul, mlp lin2: 2:4 sparse)
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half bfloat16 --device cuda --compress int8_dynamic_quant_sparse
+# int8 dynamic quant attn + int4 wo + sparse marlin lin 1 + 2:4 sparse lin2
+python eval_combo.py --coco_root_dir datasets/coco2017 --coco_slice_name val2017 --sam_checkpoint_base_path checkpoints --sam_model_type vit_h --point_sampling_cache_dir tmp/sam_coco_mask_center_cache --mask_debug_out_dir tmp/sam_eval_masks_out --batch_size 32 --num_workers 32 --use_compile max-autotune --use_half float16 --device cuda --compress int4_weight_only_sparse

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/data.py

@@ -0,0 +1,308 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+import diskcache
+import numpy as np
+import skimage.color as color
+import skimage.io as io
+import torch
+from pycocotools.coco import COCO
+from scipy import ndimage
+
+
+def _get_center_point(mask, ann_id, cache):
+    """
+    This is a rudimentary version of https://arxiv.org/pdf/2304.02643.pdf,
+    section D.1.Point Sampling
+
+    From the paper: "The first point is chosen deterministically as the point
+    farthest from the object boundary."
+
+    The code below is an approximation of this.
+
+    First, we try to calculate the center of mass. If it's inside the mask, we
+    stop here.
+
+    The centroid may be outside of the mask for some mask shapes. In this case
+    we do a slow hack, specifically, we check for the
+    minumum of the maximum distance from the boundary in four directions
+    (up, right, down, left), and take the point with the maximum of these
+    minimums. Note: this is not performant for large masks.
+
+    Returns the center point in (x, y) format
+    """
+    if ann_id in cache:
+        return cache[ann_id]
+
+    # try the center of mass, keep it if it's inside the mask
+    com_y, com_x = ndimage.center_of_mass(mask)
+    com_y, com_x = int(round(com_y, 0)), int(round(com_x, 0))
+    if mask[com_y][com_x]:
+        cache[ann_id] = (com_x, com_y)
+        return (com_x, com_y)
+
+    # if center of mass didn't work, do the slow manual approximation
+
+    # up, right, down, left
+    # TODO(future): approximate better by adding more directions
+    distances_to_check_deg = [0, 90, 180, 270]
+
+    global_min_max_distance = float("-inf")
+    global_coords = None
+    # For now, terminate early to speed up the calculation as long as
+    # the point sample is gooe enough. This sacrifices the quality of point
+    # sampling for speed. In the future we can make this more accurate.
+    DISTANCE_GOOD_ENOUGH_THRESHOLD = 20
+
+    # Note: precalculating the bounding box could be somewhat
+    #   helpful, but checked the performance gain and it's not much
+    #   so leaving it out to keep the code simple.
+    # Note: tried binary search instead of incrementing by one to
+    #   travel up/right/left/down, but that does not handle masks
+    #   with all shapes properly (there could be multiple boundaries).
+    for row_idx in range(mask.shape[0]):
+        for col_idx in range(mask.shape[1]):
+            cur_point = mask[row_idx, col_idx]
+
+            # skip points inside bounding box but outside mask
+            if not cur_point:
+                continue
+
+            max_distances = []
+            for direction in distances_to_check_deg:
+                # TODO(future) binary search instead of brute forcing it if we
+                # need a speedup, with the cache it doesn't really matter though
+                if direction == 0:
+                    # UP
+                    cur_row_idx = row_idx
+
+                    while cur_row_idx >= 0 and mask[cur_row_idx, col_idx]:
+                        cur_row_idx = cur_row_idx - 1
+                    cur_row_idx += 1
+                    distance = row_idx - cur_row_idx
+                    max_distances.append(distance)
+
+                elif direction == 90:
+                    # RIGHT
+                    cur_col_idx = col_idx
+
+                    while (
+                        cur_col_idx <= mask.shape[1] - 1 and mask[row_idx, cur_col_idx]
+                    ):
+                        cur_col_idx += 1
+                    cur_col_idx -= 1
+                    distance = cur_col_idx - col_idx
+                    max_distances.append(distance)
+
+                elif direction == 180:
+                    # DOWN
+                    cur_row_idx = row_idx
+                    while (
+                        cur_row_idx <= mask.shape[0] - 1 and mask[cur_row_idx, col_idx]
+                    ):
+                        cur_row_idx = cur_row_idx + 1
+                    cur_row_idx -= 1
+                    distance = cur_row_idx - row_idx
+                    max_distances.append(distance)
+
+                elif direction == 270:
+                    # LEFT
+                    cur_col_idx = col_idx
+                    while cur_col_idx >= 0 and mask[row_idx, cur_col_idx]:
+                        cur_col_idx -= 1
+                    cur_col_idx += 1
+                    distance = col_idx - cur_col_idx
+                    max_distances.append(distance)
+
+            min_max_distance = min(max_distances)
+            if min_max_distance > global_min_max_distance:
+                global_min_max_distance = min_max_distance
+                global_coords = (col_idx, row_idx)
+            if global_min_max_distance >= DISTANCE_GOOD_ENOUGH_THRESHOLD:
+                break
+
+    cache[ann_id] = global_coords
+    return global_coords
+
+
+def build_datapoint(
+    imgId,
+    coco,
+    pixel_mean,
+    pixel_std,
+    coco_root_dir,
+    coco_slice_name,
+    catIds,
+    cache,
+    predictor,
+    pad_input_image_batch,
+):
+    img = coco.loadImgs(imgId)[0]
+
+    file_location = f"{coco_root_dir}/{coco_slice_name}/{img['file_name']}"
+    I = io.imread(file_location)
+    if len(I.shape) == 2:
+        # some images, like img_id==61418, are grayscale
+        # convert to RGB to ensure the rest of the pipeline works
+        I = color.gray2rgb(I)
+
+    # load and display instance annotations
+    annIds = coco.getAnnIds(imgIds=img["id"], catIds=catIds, iscrowd=None)
+    anns = coco.loadAnns(annIds)
+
+    # approximate the center point of each mask
+    coords_list = []
+    gt_masks_list = []
+    for ann in anns:
+        ann_id = ann["id"]
+        mask = coco.annToMask(ann)
+        gt_masks_list.append(torch.tensor(mask))
+        coords = _get_center_point(mask, ann_id, cache)
+        coords_list.append(coords)
+
+    image = I
+
+    # predictor_set_image begin
+    # Transform the image to the form expected by the model
+    input_image = predictor.transform.apply_image(image)
+    input_image_torch = torch.as_tensor(input_image)
+    input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
+    predictor_input_size = input_image_torch.shape[-2:]
+
+    # Preprocess
+    x = input_image_torch
+    # Normalize colors
+    x = (x - pixel_mean) / pixel_std
+
+    if pad_input_image_batch:
+        # Pad
+        h, w = x.shape[-2:]
+        padh = predictor.model.image_encoder.img_size - h
+        padw = predictor.model.image_encoder.img_size - w
+        x = torch.nn.functional.pad(x, (0, padw, 0, padh))
+    else:
+        x = x.squeeze(0)
+
+    gt_masks_list = torch.stack(gt_masks_list) if len(gt_masks_list) else None
+    return image, coords_list, gt_masks_list, anns, x, predictor_input_size
+
+
+def build_data(
+    coco_img_ids,
+    coco,
+    catIds,
+    coco_root_dir,
+    coco_slice_name,
+    point_sampling_cache_dir,
+    predictor,
+    use_half,
+    pad_input_image_batch,
+):
+    cache = diskcache.Cache(point_sampling_cache_dir)
+    # make sure you clear the cache if you change the point sampling algorithm
+    # cache.clear()
+
+    pixel_mean = predictor.model.pixel_mean.cpu()
+    pixel_std = predictor.model.pixel_std.cpu()
+
+    def build_batch(indicies):
+        batch = [[], [], [], [], [], [], [], [], [], [], []]
+        batch[3] = [0]
+        batch[6] = [0]
+        for img_idx in indicies:
+            imgId = coco_img_ids[img_idx]
+
+            datapoint = build_datapoint(
+                imgId,
+                coco,
+                pixel_mean,
+                pixel_std,
+                coco_root_dir,
+                coco_slice_name,
+                catIds,
+                cache,
+                predictor,
+                pad_input_image_batch,
+            )
+            I, coords_list, gt_masks_list, anns, x, predictor_input_size = datapoint
+            if len(coords_list) == 0:
+                continue
+            batch[0].append(x)
+            # batch[0].append(x[0])
+            coords_list = predictor.transform.apply_coords(
+                np.array(coords_list), I.shape[:2]
+            )
+            coords_list = torch.tensor(coords_list, dtype=torch.float)
+
+            batch[1].append(coords_list.reshape(-1))
+            batch[2].append(coords_list.size())
+            batch[3].append(coords_list.numel() + batch[3][-1])
+
+            batch[4].append(gt_masks_list.reshape(-1))
+            batch[5].append(gt_masks_list.size())
+            batch[6].append(gt_masks_list.numel() + batch[6][-1])
+
+            batch[7].append(anns)
+            batch[8].append(I)
+            batch[9].append(predictor_input_size)
+            batch[10].append(img_idx)
+
+        def cat_and_cast(b, use_half):
+            b = torch.cat(b) if len(b) > 0 else None
+            if use_half is not None and b is not None:
+                return b.to(use_half)
+            return b
+
+        def to_nested_tensor(data, sizes=None, use_half=None):
+            if len(data) == 0:
+                return None
+            dtype = use_half if use_half is not None else torch.float32
+
+            if sizes is not None:
+                data = [d.view(s) for (d, s) in zip(data, sizes)]
+
+            return torch.nested.nested_tensor(data, dtype=dtype, layout=torch.jagged)
+
+        if pad_input_image_batch:
+            batch[0] = cat_and_cast(batch[0], use_half)
+        else:
+            batch[0] = to_nested_tensor(batch[0], use_half=use_half)
+
+        batch[1] = cat_and_cast(batch[1], use_half)
+
+        batch[4] = cat_and_cast(batch[4], False)
+
+        return batch
+
+    return build_batch
+
+
+def setup_coco_img_ids(coco_root_dir, coco_slice_name, coco_category_names, img_id):
+    annFile = "{}/annotations/instances_{}.json".format(coco_root_dir, coco_slice_name)
+
+    # initialize COCO api for instance annotations
+    coco = COCO(annFile)
+
+    # display COCO categories and supercategories
+    cats = coco.loadCats(coco.getCatIds())
+    cat_id_to_cat = {cat["id"]: cat for cat in cats}
+    # print('COCO categories: \n{}\n'.format(' '.join(nms)))
+
+    # nms = set([cat['supercategory'] for cat in cats])
+    # print('COCO supercategories: \n{}'.format(' '.join(nms)))
+
+    if coco_category_names is not None:
+        catIds = coco.getCatIds(catNms=coco_category_names)
+    else:
+        catIds = coco.getCatIds()
+
+    if img_id is not None:
+        coco_img_ids = [img_id]
+    elif coco_category_names is None:
+        coco_img_ids = coco.getImgIds()
+    else:
+        coco_img_ids = coco.getImgIds(catIds=catIds)
+
+    return coco_img_ids, cat_id_to_cat, catIds, coco

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/eval_combo.py

@@ -0,0 +1,692 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+import math
+import resource
+import time
+
+import fire
+import torch
+import tqdm
+from data import build_data, setup_coco_img_ids
+from metrics import calculate_miou, create_result_entry
+
+import torchao
+from torchao._models.utils import (
+    get_arch_name,
+    write_json_result_local,
+    write_json_result_ossci,
+)
+from torchao.dtypes import SemiSparseLayout
+from torchao.prototype.quantization.autoquant_v2 import autoquant_v2
+from torchao.quantization import (
+    autoquant,
+    int4_weight_only,
+    int8_dynamic_activation_int8_weight,
+    quantize_,
+)
+from torchao.sparsity import apply_fake_sparsity, semi_sparse_weight, sparsify_
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, unwrap_tensor_subclass
+
+torch._dynamo.config.cache_size_limit = 50000
+
+
+def unbind_jagged(device, data, sizes, offsets):
+    if data is None:
+        return None
+    data = data.to(device=device, non_blocking=True)
+    return [
+        data[offsets[batch_idx] : offsets[batch_idx + 1]].view(sizes[batch_idx])
+        for batch_idx in range(len(sizes))
+    ]
+
+
+PADDED_TENSOR = None
+
+
+# Preallocate a "landing" Tensor for incoming data and reuse it across launches.
+def pad_to_batch_size(batch, batch_size, device):
+    assert batch.dim() == 4
+    # assert batch.is_pinned()
+    global PADDED_TENSOR
+    if PADDED_TENSOR is None:
+        batch = batch.to(device=device, non_blocking=True)
+        full_batch_size = (batch_size, batch.size(1), batch.size(2), batch.size(3))
+        first_entry = batch[0].unsqueeze(0)
+        repeat_first_entry = first_entry.expand(full_batch_size)
+        padded_batch = torch.cat(
+            [batch, repeat_first_entry[batch.size(0) : batch_size]], dim=0
+        )
+        assert padded_batch.size() == full_batch_size
+        PADDED_TENSOR = padded_batch
+    PADDED_TENSOR[: batch.size(0)].copy_(batch, non_blocking=True)
+    return PADDED_TENSOR
+
+
+def get_features_batch(
+    encoder, input_image_batch, pad_input_image_batch, batch_size, device
+):
+    if pad_input_image_batch:
+        features_batch = encoder(
+            pad_to_batch_size(input_image_batch, batch_size, device)
+        )
+        return features_batch[: input_image_batch.size(0)]
+    return encoder(input_image_batch)
+
+
+def build_results_batch(predictor, batch, batch_size, pad_input_image_batch):
+    encoder = predictor.model.image_encoder
+    device = predictor.device
+
+    input_image_batch = batch[0]
+    # The number of valid data points varies slightly per batch
+    orig_input_image_batch_size = input_image_batch.size(0)
+    if input_image_batch is None:
+        return (None, None, None)
+
+    with torch.autograd.profiler.record_function("data transfer"):
+        coords_lists = unbind_jagged(*([device] + batch[1:4]))
+        gt_masks_lists = unbind_jagged(*([device] + batch[4:7]))
+        if coords_lists is None:
+            return (None, None, None)
+        datapoints = list(zip(*(batch[7:] + [coords_lists, gt_masks_lists])))
+        if pad_input_image_batch:
+            # Pad to a static shape to avoid recompilation
+            input_image_batch = pad_to_batch_size(input_image_batch, batch_size, device)
+        else:
+            input_image_batch = input_image_batch.to(device=device, non_blocking=True)
+
+    # We explicitly exclude data transfers from the timing to focus
+    # only on the kernel performance.
+    # Next we synchronize and set two events to start timing.
+    if torch.cuda.is_available():
+        torch.cuda.synchronize()
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+        start_event.record()
+    else:
+        t0 = time.time()
+
+    with torch.autograd.profiler.record_function("timed region"):
+        with torch.autograd.profiler.record_function("image encoder"):
+            features_batch = encoder(input_image_batch)
+            features_batch = features_batch[:orig_input_image_batch_size]
+
+        with torch.autograd.profiler.record_function("predict_torch"):
+            result_batch = []
+            for batch_idx, (
+                anns,
+                image,
+                input_size,
+                idx,
+                coords,
+                gt_masks,
+            ) in enumerate(datapoints):
+                features = features_batch.narrow(0, batch_idx, 1)
+                predictor.reset_image()
+                predictor.original_size = image.shape[:2]
+                predictor.input_size = input_size
+                predictor.features = features
+                predictor.is_image_set = True
+                coords = coords.unsqueeze(1)
+                fg_labels = torch.ones(
+                    (coords.size(0), 1), dtype=torch.int, device=device
+                )
+                masks, scores, logits = predictor.predict_torch(
+                    point_coords=coords,
+                    point_labels=fg_labels,
+                    multimask_output=True,
+                )
+                entry = create_result_entry(anns, gt_masks, masks, scores, idx)
+                result_batch += entry
+
+        # After all kernels have been launched we synchronize again and measure
+        # the amount of time spent on the GPU. This is a fairly tight measurement
+        # around the launched GPU kernels and excludes data movement from host
+        # to device.
+        if torch.cuda.is_available():
+            end_event.record()
+            torch.cuda.synchronize()
+            elapsed_time = start_event.elapsed_time(end_event)
+        else:
+            elapsed_time = time.time() - t0
+    return result_batch, orig_input_image_batch_size, elapsed_time
+
+
+def build_results(
+    batched_data_iter,
+    predictor,
+    mask_debug_out_dir,
+    batch_size,
+    use_compile,
+    use_compile_decoder,
+    pad_input_image_batch,
+    compress,
+    use_fullgraph=False,
+):
+    # TODO: Re-enable this for datapoints
+    assert not use_compile_decoder
+
+    batch_runner = build_results_batch
+
+    results = []
+    batch_idx = 0
+    num_images = 0
+    num_batches = 0
+    elapsed_time = 0
+    partial_batch = False
+    for batch in tqdm.tqdm(batched_data_iter):
+        with torch.no_grad():
+            if batch_idx == 0:
+                with torch.autograd.profiler.record_function("compilation and warmup"):
+                    if str(use_compile) != "False":
+                        predictor.model.image_encoder = torch.compile(
+                            predictor.model.image_encoder,
+                            mode=use_compile,
+                            fullgraph=use_fullgraph,
+                        )
+                    # Run first batch a few times for warmup and exclude it from the final timings
+                    for _ in range(5):
+                        _ = batch_runner(
+                            predictor, batch, batch_size, pad_input_image_batch
+                        )
+            result_batch, num_datapoints, kernel_time = batch_runner(
+                predictor, batch, batch_size, pad_input_image_batch
+            )
+            if result_batch is not None:
+                results += result_batch
+        # We expect a partial batch to only happens once at the end
+        assert not partial_batch
+        # Only measure timing on full batches
+        if num_datapoints == batch_size:
+            num_images += num_datapoints
+            num_batches += 1
+            # We consistently exclude the last (512 - filtered) images
+            # Since batch sizes must be powers of two and less than
+            # or equal 512 this ensures consistent timing across varying
+            # batch sizes.
+            if num_images <= 4488:
+                elapsed_time += kernel_time
+        else:
+            partial_batch = True
+        batch_idx += 1
+
+    avg_ms_per_img = None
+    if num_images > 0:
+        avg_ms_per_img = elapsed_time
+        avg_ms_per_img = avg_ms_per_img / num_images
+
+    return results, avg_ms_per_img, num_batches, num_images
+
+
+def identity_runner(fn, *args, **kwargs):
+    return fn(*args, **kwargs)
+
+
+def profiler_runner(path, fn, *args, **kwargs):
+    with torch.profiler.profile(
+        activities=[
+            torch.profiler.ProfilerActivity.CPU,
+            torch.profiler.ProfilerActivity.CUDA,
+        ],
+        record_shapes=True,
+    ) as prof:
+        result = fn(*args, **kwargs)
+    prof.export_chrome_trace(path)
+    return result
+
+
+def profile_top_runner(fn, *args, **kwargs):
+    with torch.profiler.profile(
+        activities=[
+            torch.profiler.ProfilerActivity.CPU,
+            torch.profiler.ProfilerActivity.CUDA,
+        ],
+        record_shapes=True,
+    ) as prof:
+        result = fn(*args, **kwargs)
+    if torch.cuda.is_available():
+        print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=-1))
+    else:
+        print(prof.key_averages().table(sort_by="self_cpu_time_total", row_limit=-1))
+    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 run(
+    coco_root_dir,
+    coco_slice_name,
+    sam_checkpoint_base_path,
+    sam_model_type,
+    point_sampling_cache_dir,
+    mask_debug_out_dir,
+    batch_size=1,
+    print_header=False,
+    coco_category_names=None,
+    limit=None,
+    img_id=None,
+    use_half=None,
+    use_compile="False",
+    use_compile_decoder=False,
+    compress=None,
+    min_sqnr=None,
+    num_workers=0,
+    use_rel_pos=True,
+    pad_input_image_batch=True,
+    profile_path=None,
+    profile_top=False,
+    memory_path=None,
+    device="cuda",
+    output_json_path=None,
+    output_json_local=False,
+):
+    from torch._inductor import config as inductorconfig
+
+    inductorconfig.triton.unique_kernel_names = True
+    inductorconfig.epilogue_fusion = True
+    inductorconfig.coordinate_descent_tuning = True
+    inductorconfig.coordinate_descent_check_all_directions = True
+    inductorconfig.force_fuse_int_mm_with_mul = True
+    inductorconfig.use_mixed_mm = True
+    from torch.sparse import SparseSemiStructuredTensor
+
+    SparseSemiStructuredTensor._FORCE_CUTLASS = False
+
+    if use_half is not None:
+        if use_half == "float16":
+            use_half = torch.float16
+        elif use_half == "bfloat16":
+            use_half = torch.bfloat16
+        else:
+            raise ValueError(
+                "Expected one of float16 or bfloat for specified {use_half}"
+            )
+
+    # Batch size needs to be a multiple of two and at most 512.
+    assert math.log2(batch_size).is_integer()
+    assert batch_size <= 512
+
+    # https://github.com/facebookresearch/segment-anything/tree/main#model-checkpoints
+    # largest to smallest: vit_h, vit_l, vit_b
+    model_type_to_checkpoint = {
+        "vit_h": f"{sam_checkpoint_base_path}/sam_vit_h_4b8939.pth",
+        "vit_l": f"{sam_checkpoint_base_path}/sam_vit_l_0b3195.pth",
+        "vit_b": f"{sam_checkpoint_base_path}/sam_vit_b_01ec64.pth",
+    }
+
+    from segment_anything_fast import SamPredictor, sam_model_registry
+
+    checkpoint_path = model_type_to_checkpoint[sam_model_type]
+    sam = sam_model_registry[sam_model_type](checkpoint=checkpoint_path).to(
+        torch.device(device)
+    )
+    predictor = SamPredictor(sam)
+
+    from segment_anything_fast import tools
+
+    tools.apply_eval_dtype_predictor(predictor, use_half)
+
+    for block in predictor.model.image_encoder.blocks:
+        block.attn.use_rel_pos = use_rel_pos
+
+    # Helper filter functions
+    def attn_only(mod, name):
+        return isinstance(mod, torch.nn.Linear) and "attn" in name
+
+    def mlp_lin1_only(mod, name):
+        return isinstance(mod, torch.nn.Linear) and "lin1" in name
+
+    def mlp_lin2_only(mod, name):
+        return isinstance(mod, torch.nn.Linear) and "lin2" in name
+
+    def mlp_only(mod, name):
+        return isinstance(mod, torch.nn.Linear) and "mlp" in name
+
+    if compress == "int8_dynamic_quant":
+        quantize_(predictor.model.image_encoder, int8_dynamic_activation_int8_weight())
+        if not TORCH_VERSION_AT_LEAST_2_5:
+            predictor.model.image_encoder = unwrap_tensor_subclass(
+                predictor.model.image_encoder
+            )
+    elif compress == "sparse_mlp_only":
+
+        def mlp_only(mod, name):
+            return isinstance(mod, torch.nn.Linear) and "mlp" in name
+
+        apply_fake_sparsity(predictor.model.image_encoder, filter_fn=mlp_only)
+        sparsify_(
+            predictor.model.image_encoder, semi_sparse_weight(), filter_fn=mlp_only
+        )
+    elif compress == "sparse":
+        apply_fake_sparsity(predictor.model.image_encoder)
+        sparsify_(predictor.model.image_encoder, semi_sparse_weight())
+    elif compress == "int8_dynamic_quant_sparse":
+        # apply sparsify first to set qparams
+        apply_fake_sparsity(predictor.model.image_encoder, filter_fn=mlp_only)
+
+        quantize_(
+            predictor.model.image_encoder,
+            int8_dynamic_activation_int8_weight(),
+            attn_only,
+        )
+        quantize_(
+            predictor.model.image_encoder,
+            int8_dynamic_activation_int8_weight(layout=SemiSparseLayout()),
+            mlp_lin1_only,
+        )
+        sparsify_(predictor.model.image_encoder, semi_sparse_weight(), mlp_lin2_only)
+        if not TORCH_VERSION_AT_LEAST_2_5:
+            predictor.model.image_encoder = unwrap_tensor_subclass(
+                predictor.model.image_encoder
+            )
+    elif compress == "int4_weight_only_sparse":
+        # apply sparsify first to set qparams
+        apply_fake_sparsity(predictor.model.image_encoder, filter_fn=mlp_only)
+        from torchao.dtypes import MarlinSparseLayout
+
+        quantize_(
+            predictor.model.image_encoder,
+            int8_dynamic_activation_int8_weight(),
+            attn_only,
+        )
+        quantize_(
+            predictor.model.image_encoder,
+            int4_weight_only(layout=MarlinSparseLayout()),
+            mlp_lin1_only,
+        )
+        sparsify_(predictor.model.image_encoder, semi_sparse_weight(), mlp_lin2_only)
+        if not TORCH_VERSION_AT_LEAST_2_5:
+            predictor.model.image_encoder = unwrap_tensor_subclass(
+                predictor.model.image_encoder
+            )
+
+    elif compress is not None and "autoquant_v2" in compress:
+        example_input = torch.randn(
+            1, 3, 1024, 1024, dtype=torch.bfloat16, device=device
+        )
+        if "autoquant_v2-int4" == compress:
+            autoquant_v2(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.prototype.quantization.autoquant_v2.DEFAULT_INT4_AUTOQUANT_CLASS_LIST,
+            )
+        elif "autoquant_v2-float8" == compress:
+            autoquant_v2(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.prototype.quantization.autoquant_v2.OTHER_AUTOQUANT_CLASS_LIST,
+            )
+        elif "autoquant_v2-all" == compress:
+            autoquant_v2(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.prototype.quantization.autoquant_v2.ALL_AUTOQUANT_CLASS_LIST,
+            )
+        else:
+            autoquant_v2(
+                predictor.model.image_encoder, example_input=example_input, manual=True
+            )
+
+        predictor.model.image_encoder(example_input)
+        predictor.model.image_encoder.finalize_autoquant()
+
+    elif compress is not None and "autoquant" in compress:
+        example_input = torch.randn(
+            1, 3, 1024, 1024, dtype=torch.bfloat16, device=device
+        )
+        if "autoquant-int4" == compress:
+            autoquant(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.quantization.DEFAULT_INT4_AUTOQUANT_CLASS_LIST,
+                min_sqnr=min_sqnr,
+            )
+        elif "autoquant-float8" == compress:
+            autoquant(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.quantization.OTHER_AUTOQUANT_CLASS_LIST,
+                min_sqnr=min_sqnr,
+            )
+        elif "autoquant-sparse" == compress:
+            autoquant(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.quantization.DEFAULT_SPARSE_AUTOQUANT_CLASS_LIST,
+                min_sqnr=min_sqnr,
+            )
+        elif "autoquant-all" == compress:
+            autoquant(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                qtensor_class_list=torchao.quantization.ALL_AUTOQUANT_CLASS_LIST,
+                min_sqnr=min_sqnr,
+            )
+        else:
+            autoquant(
+                predictor.model.image_encoder,
+                example_input=example_input,
+                manual=True,
+                min_sqnr=min_sqnr,
+            )
+        predictor.model.image_encoder(example_input)
+        predictor.model.image_encoder.finalize_autoquant()
+    else:
+        assert compress is None, f"Unsupported compress mode {compress}"
+
+    coco_img_ids_, cat_id_to_cat, catIds, coco = setup_coco_img_ids(
+        coco_root_dir, coco_slice_name, coco_category_names, img_id
+    )
+
+    coco_img_ids = []
+    for imgId in coco_img_ids_:
+        img = coco.loadImgs(imgId)[0]
+        annIds = coco.getAnnIds(imgIds=img["id"], catIds=catIds, iscrowd=None)
+        anns = coco.loadAnns(annIds)
+        if len(anns) != 0:
+            coco_img_ids.append(imgId)
+
+    build_batch = build_data(
+        coco_img_ids,
+        coco,
+        catIds,
+        coco_root_dir,
+        coco_slice_name,
+        point_sampling_cache_dir,
+        predictor,
+        use_half,
+        pad_input_image_batch,
+    )
+
+    limit = len(coco_img_ids) if limit is None else limit
+    batched_data_iter = torch.utils.data.DataLoader(
+        list(range(limit)),
+        batch_size=batch_size,
+        collate_fn=build_batch,
+        num_workers=num_workers,
+        pin_memory=False,
+    )
+    runner = identity_runner
+
+    if profile_path is not None:
+        import functools
+
+        runner = functools.partial(profiler_runner, profile_path)
+
+    if profile_top:
+        runner = profile_top_runner
+
+    if memory_path is not None:
+        assert use_compile != "max-autotune", (
+            f"Memory path does not support {use_compile}"
+        )
+        import functools
+
+        runner = functools.partial(memory_runner, memory_path)
+
+    results, avg_ms_per_img, num_batches, num_images = runner(
+        build_results,
+        batched_data_iter,
+        predictor,
+        mask_debug_out_dir,
+        batch_size,
+        use_compile,
+        use_compile_decoder,
+        pad_input_image_batch,
+        compress,
+    )
+
+    results = [[r[0], r[1], r[2], r[3].item()] for r in results]
+
+    img_s, batch_ms_batch_size = None, None
+    if avg_ms_per_img is not None:
+        img_s = 1000 / avg_ms_per_img
+        batch_ms_batch_size = (avg_ms_per_img * num_images) / num_batches / batch_size
+
+    mIoU = calculate_miou(results, mask_debug_out_dir, True, cat_id_to_cat)
+    if torch.cuda.is_available():
+        max_memory_allocated_bytes = torch.cuda.max_memory_allocated()
+        _, total_memory = torch.cuda.mem_get_info()
+        max_memory_allocated_percentage = int(
+            100 * (max_memory_allocated_bytes / total_memory)
+        )
+        max_memory_allocated_bytes = max_memory_allocated_bytes >> 20
+    else:
+        import psutil
+
+        total_memory = psutil.virtual_memory().total
+        max_memory_allocated_bytes = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+        max_memory_allocated_percentage = int(
+            100 * (max_memory_allocated_bytes / (total_memory >> 10))
+        )
+        max_memory_allocated_bytes = max_memory_allocated_bytes >> 10
+
+    with open("results.csv", "a") as f:
+        if print_header:
+            header = ",".join(
+                [
+                    "device",
+                    "sam_model_type",
+                    "batch_size",
+                    "memory(MiB)",
+                    "memory(%)",
+                    "img_s(avg)",
+                    "batch_ms(avg)/batch_size",
+                    "mIoU",
+                    "use_compile",
+                    "use_half",
+                    "compress",
+                    "use_compile_decoder",
+                    "use_rel_pos",
+                    "pad_input_image_batch",
+                    "num_workers",
+                    "num_batches",
+                    "num_images",
+                    "profile_path",
+                    "memory_path",
+                ]
+            )
+            f.write(header + "\n")
+        vals = ",".join(
+            map(
+                str,
+                [
+                    device,
+                    sam_model_type,
+                    batch_size,
+                    max_memory_allocated_bytes,
+                    max_memory_allocated_percentage,
+                    img_s,
+                    batch_ms_batch_size,
+                    mIoU,
+                    use_compile,
+                    use_half,
+                    compress,
+                    use_compile_decoder,
+                    use_rel_pos,
+                    pad_input_image_batch,
+                    num_workers,
+                    num_batches,
+                    num_images,
+                    profile_path,
+                    memory_path,
+                ],
+            )
+        )
+        f.write(vals + "\n")
+
+    if output_json_path:
+        headers = [
+            "name",
+            "dtype",
+            "min_sqnr",
+            "compile",
+            "device",
+            "arch",
+            "metric",
+            "actual",
+            "target",
+        ]
+        name = sam_model_type
+        arch = get_arch_name()
+        dtype = compress or "noquant"
+        # boolean flag to indicate whether compile is used
+        compile = use_compile != "False"
+        memory_result = [
+            name,
+            dtype,
+            min_sqnr,
+            compile,
+            device,
+            arch,
+            "memory(MiB)",
+            max_memory_allocated_bytes,
+            None,
+        ]
+        performance_result = [
+            name,
+            dtype,
+            min_sqnr,
+            compile,
+            device,
+            arch,
+            "img_s(avg)",
+            img_s,
+            None,
+        ]
+        write_json_result = (
+            write_json_result_local if output_json_local else write_json_result_ossci
+        )
+        write_json_result(output_json_path, headers, memory_result)
+        write_json_result(output_json_path, headers, performance_result)
+
+
+if __name__ == "__main__":
+    fire.Fire(run)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/metrics.py

@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+import pandas as pd
+import torch
+
+
+def create_result_entry(anns, gt_masks_list, masks, scores, img_idx):
+    argmax_scores = torch.argmax(scores, dim=1)
+    inference_masks = masks.gather(
+        1,
+        argmax_scores.unsqueeze(-1)
+        .unsqueeze(-1)
+        .unsqueeze(-1)
+        .expand((masks.size(0), 1, masks.size(2), masks.size(3))),
+    ).squeeze(1)
+
+    def _iou(mask1, mask2):
+        assert mask1.dim() == 3
+        assert mask2.dim() == 3
+        intersection = torch.logical_and(mask1, mask2)
+        union = torch.logical_or(mask1, mask2)
+        return intersection.sum(dim=(-1, -2)) / union.sum(dim=(-1, -2))
+
+    top_score_ious = _iou(inference_masks, gt_masks_list)
+
+    entry = []
+    for idx in range(top_score_ious.size(0)):
+        entry.append(
+            [img_idx, anns[idx]["id"], anns[idx]["category_id"], top_score_ious[idx]]
+        )
+    return entry
+
+
+def calculate_miou(results, mask_debug_out_dir, silent, cat_id_to_cat):
+    df = pd.DataFrame(results, columns=["img_id", "ann_id", "cat_id", "iou"])
+    df.to_csv(f"{mask_debug_out_dir}/df.csv")
+    df["supercategory"] = df["cat_id"].map(
+        lambda cat_id: cat_id_to_cat[cat_id]["supercategory"]
+    )
+    df["category"] = df["cat_id"].map(lambda cat_id: cat_id_to_cat[cat_id]["name"])
+
+    # TODO: cross reference the specifics of how we calculate mIoU with
+    # the SAM folks (should it be per dataset, per category, per image, etc)
+    # currently, just calculate them all
+
+    # TODO: QOL save the summaries to file
+
+    # per category
+    per_category = pd.pivot_table(
+        df,
+        values="iou",
+        index=["cat_id", "supercategory", "category"],
+        aggfunc=("mean", "count"),
+    )
+    if not silent:
+        print("\nmIoU averaged per category")
+        print(per_category)
+
+    # per super-category
+    per_supercategory = pd.pivot_table(
+        df, values="iou", index=["supercategory"], aggfunc=("mean", "count")
+    )
+    if not silent:
+        print("\nmIoU averaged per supercategory")
+        print(per_supercategory)
+
+    # per all selected masks
+    per_all_masks_agg = df["iou"].agg(["mean", "count"])
+    if not silent:
+        print("\nmIoU averaged per all selected masks")
+        print(per_all_masks_agg)
+
+    return df["iou"].agg(["mean", "count"])["mean"]

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/results.csv

@@ -0,0 +1,7 @@
+device,sam_model_type,batch_size,memory(MiB),memory(%),img_s(avg),batch_ms(avg)/batch_size,mIoU,use_compile,use_half,compress,use_compile_decoder,use_rel_pos,pad_input_image_batch,num_workers,num_batches,num_images,profile_path,memory_path
+cuda,vit_h,32,15172,18,22.533401716616083,44.37856354651513,0.5812715827356921,max-autotune,torch.bfloat16,None,False,True,True,32,154,4928,None,None
+cuda,vit_h,32,15154,18,25.16516896830006,39.73746416166231,0.5818834536577897,max-autotune,torch.bfloat16,int8_dynamic_quant,False,True,True,32,154,4928,None,None
+cuda,vit_h,32,15632,19,24.824717871078573,40.282431614863405,0.5675837487618974,max-autotune,torch.bfloat16,sparse_mlp_only,False,True,True,32,154,4928,None,None
+cuda,vit_h,32,13429,16,24.589577947798148,40.66763578142439,0.5306639662569573,max-autotune,torch.bfloat16,sparse,False,True,True,32,154,4928,None,None
+cuda,vit_h,32,14869,18,26.597207143088742,37.597932543073384,0.5669944616184625,max-autotune,torch.bfloat16,int8_dynamic_quant_sparse,False,True,True,32,154,4928,None,None
+cuda,vit_h,32,17068,21,23.96093702681232,41.73459489004953,0.5485481164943489,max-autotune,torch.float16,int4_weight_only_sparse,False,True,True,32,154,4928,None,None

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam/setup.sh

@@ -0,0 +1,26 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+
+SETUP_HOME=$(pwd)
+
+
+mkdir -p checkpoints
+mkdir -p datasets
+
+mkdir -p tmp
+mkdir -p tmp/sam_coco_mask_center_cache
+mkdir -p tmp/sam_eval_masks_out
+
+wget -nc -P checkpoints https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
+wget -nc -P checkpoints https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
+wget -nc -P checkpoints https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth
+
+mkdir -p datasets/coco2017
+wget -nc -P datasets/coco2017 http://images.cocodataset.org/zips/val2017.zip
+wget -nc -P datasets/coco2017 http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+
+cd datasets/coco2017 && unzip -n val2017.zip && cd $SETUP_HOME
+cd datasets/coco2017 && unzip -n annotations_trainval2017.zip && cd $SETUP_HOME

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/__init__.py

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from hydra import initialize_config_module
+from hydra.core.global_hydra import GlobalHydra
+
+if not GlobalHydra.instance().is_initialized():
+    initialize_config_module("torchao._models.sam2", version_base="1.2")

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/automatic_mask_generator.py

@@ -0,0 +1,759 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from torchvision.ops.boxes import batched_nms, box_area  # type: ignore
+
+from torchao._models.sam2.modeling.sam2_base import SAM2Base
+from torchao._models.sam2.sam2_image_predictor import SAM2ImagePredictor
+from torchao._models.sam2.utils.amg import (
+    MaskData,
+    _mask_to_rle_pytorch_2_0,
+    _mask_to_rle_pytorch_2_1,
+    area_from_rle,
+    batch_iterator,
+    batched_mask_to_box,
+    box_xyxy_to_xywh,
+    build_all_layer_point_grids,
+    calculate_stability_score,
+    coco_encode_rle,
+    generate_crop_boxes,
+    is_box_near_crop_edge_torch,
+    mask_to_rle_pytorch,
+    remove_small_regions,
+    rle_to_mask,
+    uncrop_boxes_xyxy,
+    uncrop_masks,
+    uncrop_points,
+)
+from torchao._models.sam2.utils.misc import (
+    crop_image,
+    get_image_size,
+)
+
+
+class SAM2AutomaticMaskGenerator(torch.nn.Module):
+    def __init__(
+        self,
+        model: SAM2Base,
+        points_per_side: Optional[int] = 32,
+        points_per_batch: int = 64,
+        pred_iou_thresh: float = 0.8,
+        stability_score_thresh: float = 0.95,
+        stability_score_offset: float = 1.0,
+        mask_threshold: float = 0.0,
+        box_nms_thresh: float = 0.7,
+        crop_n_layers: int = 0,
+        crop_nms_thresh: float = 0.7,
+        crop_overlap_ratio: float = 512 / 1500,
+        crop_n_points_downscale_factor: int = 1,
+        point_grids: Optional[List[np.ndarray]] = None,
+        min_mask_region_area: int = 0,
+        output_mode: str = "binary_mask",
+        use_m2m: bool = False,
+        multimask_output: bool = True,
+        **kwargs,
+    ) -> None:
+        """
+        Using a SAM 2 model, generates masks for the entire image.
+        Generates a grid of point prompts over the image, then filters
+        low quality and duplicate masks. The default settings are chosen
+        for SAM 2 with a HieraL backbone.
+
+        Arguments:
+          model (Sam): The SAM 2 model to use for mask prediction.
+          points_per_side (int or None): The number of points to be sampled
+            along one side of the image. The total number of points is
+            points_per_side**2. If None, 'point_grids' must provide explicit
+            point sampling.
+          points_per_batch (int): Sets the number of points run simultaneously
+            by the model. Higher numbers may be faster but use more GPU memory.
+          pred_iou_thresh (float): A filtering threshold in [0,1], using the
+            model's predicted mask quality.
+          stability_score_thresh (float): A filtering threshold in [0,1], using
+            the stability of the mask under changes to the cutoff used to binarize
+            the model's mask predictions.
+          stability_score_offset (float): The amount to shift the cutoff when
+            calculated the stability score.
+          mask_threshold (float): Threshold for binarizing the mask logits
+          box_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks.
+          crop_n_layers (int): If >0, mask prediction will be run again on
+            crops of the image. Sets the number of layers to run, where each
+            layer has 2**i_layer number of image crops.
+          crop_nms_thresh (float): The box IoU cutoff used by non-maximal
+            suppression to filter duplicate masks between different crops.
+          crop_overlap_ratio (float): Sets the degree to which crops overlap.
+            In the first crop layer, crops will overlap by this fraction of
+            the image length. Later layers with more crops scale down this overlap.
+          crop_n_points_downscale_factor (int): The number of points-per-side
+            sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
+          point_grids (list(np.ndarray) or None): A list over explicit grids
+            of points used for sampling, normalized to [0,1]. The nth grid in the
+            list is used in the nth crop layer. Exclusive with points_per_side.
+          min_mask_region_area (int): If >0, postprocessing will be applied
+            to remove disconnected regions and holes in masks with area smaller
+            than min_mask_region_area. Requires opencv.
+          output_mode (str): The form masks are returned in. Can be 'binary_mask',
+            'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.
+            For large resolutions, 'binary_mask' may consume large amounts of
+            memory.
+          use_m2m (bool): Whether to add a one step refinement using previous mask predictions.
+          multimask_output (bool): Whether to output multimask at each point of the grid.
+        """
+        super().__init__()
+        assert (points_per_side is None) != (point_grids is None), (
+            "Exactly one of points_per_side or point_grid must be provided."
+        )
+        if points_per_side is not None:
+            self.point_grids = build_all_layer_point_grids(
+                points_per_side,
+                crop_n_layers,
+                crop_n_points_downscale_factor,
+            )
+        elif point_grids is not None:
+            self.point_grids = point_grids
+        else:
+            raise ValueError("Can't have both points_per_side and point_grid be None.")
+
+        assert output_mode in [
+            "binary_mask",
+            "uncompressed_rle",
+            "coco_rle",
+        ], f"Unknown output_mode {output_mode}."
+        if output_mode == "coco_rle":
+            try:
+                from pycocotools import mask as mask_utils  # type: ignore  # noqa: F401
+            except ImportError as e:
+                print("Please install pycocotools")
+                raise e
+
+        self.predictor = SAM2ImagePredictor(
+            model,
+            max_hole_area=min_mask_region_area,
+            max_sprinkle_area=min_mask_region_area,
+        )
+        self.points_per_batch = points_per_batch
+        self.pred_iou_thresh = pred_iou_thresh
+        self.stability_score_thresh = stability_score_thresh
+        self.stability_score_offset = stability_score_offset
+        self.mask_threshold = mask_threshold
+        self.box_nms_thresh = box_nms_thresh
+        self.crop_n_layers = crop_n_layers
+        self.crop_nms_thresh = crop_nms_thresh
+        self.crop_overlap_ratio = crop_overlap_ratio
+        self.crop_n_points_downscale_factor = crop_n_points_downscale_factor
+        self.min_mask_region_area = min_mask_region_area
+        self.output_mode = output_mode
+        self.use_m2m = use_m2m
+        self.multimask_output = multimask_output
+
+        # Store a reference to these on the model so I can overwrite them
+        # with compile annotation if desired
+
+        self.calculate_stability_score = calculate_stability_score
+        self.batched_mask_to_box = batched_mask_to_box
+
+    @classmethod
+    def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2AutomaticMaskGenerator":
+        """
+        Load a pretrained model from the Hugging Face hub.
+
+        Arguments:
+          model_id (str): The Hugging Face repository ID.
+          **kwargs: Additional arguments to pass to the model constructor.
+
+        Returns:
+          (SAM2AutomaticMaskGenerator): The loaded model.
+        """
+        from sam2.build_sam import build_sam2_hf
+
+        sam_model = build_sam2_hf(model_id, **kwargs)
+        return cls(sam_model, **kwargs)
+
+    @torch.no_grad()
+    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Generates masks for the given image.
+
+        Arguments:
+          image (np.ndarray): The image to generate masks for, in HWC uint8 format.
+
+        Returns:
+           list(dict(str, any)): A list over records for masks. Each record is
+             a dict containing the following keys:
+               segmentation (dict(str, any) or np.ndarray): The mask. If
+                 output_mode='binary_mask', is an array of shape HW. Otherwise,
+                 is a dictionary containing the RLE.
+               bbox (list(float)): The box around the mask, in XYWH format.
+               area (int): The area in pixels of the mask.
+               predicted_iou (float): The model's own prediction of the mask's
+                 quality. This is filtered by the pred_iou_thresh parameter.
+               point_coords (list(list(float))): The point coordinates input
+                 to the model to generate this mask.
+               stability_score (float): A measure of the mask's quality. This
+                 is filtered on using the stability_score_thresh parameter.
+               crop_box (list(float)): The crop of the image used to generate
+                 the mask, given in XYWH format.
+        """
+
+        # Generate masks
+        mask_data = self._generate_masks(image)
+
+        return self._encode_masks(mask_data)
+
+    def _encode_masks(self, mask_data):
+        mask_data["rles"] = _mask_to_rle_pytorch_2_1(mask_data["rles"])
+        # Encode masks
+        if self.output_mode == "coco_rle":
+            mask_data["segmentations"] = [
+                coco_encode_rle(rle) for rle in mask_data["rles"]
+            ]
+        elif self.output_mode == "binary_mask":
+            mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
+        else:
+            mask_data["segmentations"] = mask_data["rles"]
+
+        # Write mask records
+        curr_anns = []
+        for idx in range(len(mask_data["segmentations"])):
+            ann = {
+                "segmentation": mask_data["segmentations"][idx],
+                "area": area_from_rle(mask_data["rles"][idx]),
+                "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
+                "predicted_iou": mask_data["iou_preds"][idx].item(),
+                "point_coords": [mask_data["points"][idx].tolist()],
+                "stability_score": mask_data["stability_score"][idx].item(),
+                "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
+            }
+            curr_anns.append(ann)
+
+        return curr_anns
+
+    @torch.no_grad()
+    def generate_batch(self, images: List[np.ndarray]) -> List[List[Dict[str, Any]]]:
+        data = self._generate_masks_batch(images)
+        return [self._encode_masks(d) for d in data]
+
+    def _generate_masks(self, image: Union[np.ndarray, torch.Tensor]) -> MaskData:
+        orig_size = get_image_size(image)
+        crop_boxes, layer_idxs = generate_crop_boxes(
+            orig_size, self.crop_n_layers, self.crop_overlap_ratio
+        )
+
+        # Iterate over image crops
+        data = None
+        for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
+            crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)
+            if data is None:
+                data = crop_data
+            else:
+                data.cat(crop_data)
+
+        return self._deduplicate_masks(crop_boxes, data)
+
+    def _deduplicate_masks(self, crop_boxes, data):
+        # Remove duplicate masks between crops
+        if len(crop_boxes) > 1:
+            # Prefer masks from smaller crops
+            scores = 1 / box_area(data["crop_boxes"])
+            scores = scores.to(data["boxes"].device)
+            keep_by_nms = batched_nms(
+                data["boxes"].float(),
+                scores,
+                torch.zeros_like(data["boxes"][:, 0]),  # categories
+                iou_threshold=self.crop_nms_thresh,
+            )
+            data.filter(keep_by_nms)
+        data.to_numpy()
+        return data
+
+    def _generate_masks_batch(self, images: List[np.ndarray]) -> List[MaskData]:
+        all_orig_size = []
+        all_crop_boxes = []
+        all_layer_idxs = []
+        for image in images:
+            orig_size = get_image_size(image)
+            all_orig_size.append(orig_size)
+            crop_boxes, layer_idxs = generate_crop_boxes(
+                orig_size, self.crop_n_layers, self.crop_overlap_ratio
+            )
+            all_crop_boxes.append(crop_boxes)
+            all_layer_idxs.append(layer_idxs)
+
+        all_data = self._process_crop_batch(
+            images, all_crop_boxes, all_layer_idxs, all_orig_size
+        )
+
+        return [
+            self._deduplicate_masks(crop_boxes, data)
+            for (crop_boxes, data) in zip(all_crop_boxes, all_data)
+        ]
+
+    def _process_crop(
+        self,
+        image: np.ndarray,
+        crop_box: List[int],
+        crop_layer_idx: int,
+        orig_size: Tuple[int, ...],
+    ) -> MaskData:
+        # Crop the image and calculate embeddings
+        cropped_im = crop_image(image, crop_box)
+        cropped_im_size = get_image_size(cropped_im)
+        with torch.autograd.profiler.record_function("set_image"):
+            self.predictor.set_image(cropped_im)
+
+        return self._process_crop_points(
+            cropped_im_size, crop_layer_idx, crop_box, orig_size
+        )
+
+    def _process_crop_points(
+        self, cropped_im_size, crop_layer_idx, crop_box, orig_size
+    ):
+        # Get points for this crop
+        points_scale = np.array(cropped_im_size)[None, ::-1]
+        points_for_image = self.point_grids[crop_layer_idx] * points_scale
+
+        # Generate masks for this crop in batches
+        # data = MaskData()
+        data = None
+        points_per_batch = self.points_per_batch
+        if self.points_per_batch is None:
+            points_per_batch = len(points_for_image)
+        for (points,) in batch_iterator(points_per_batch, points_for_image):
+            batch_data = self._process_batch(
+                points, cropped_im_size, crop_box, orig_size, normalize=True
+            )
+            with torch.autograd.profiler.record_function("data.cat"):
+                if data is None:
+                    data = batch_data
+                else:
+                    data.cat(batch_data)
+                    del batch_data
+        self.predictor.reset_predictor()
+        return self._process_crop_points_dedup(data, crop_box)
+
+    def _process_crop_points_dedup(self, data, crop_box):
+        with torch.autograd.profiler.record_function("batched_nms"):
+            # Remove duplicates within this crop.
+            keep_by_nms = batched_nms(
+                data["boxes"].float(),
+                data["iou_preds"],
+                torch.zeros_like(data["boxes"][:, 0]),  # categories
+                iou_threshold=self.box_nms_thresh,
+            )
+
+        with torch.autograd.profiler.record_function("filter"):
+            data.filter(keep_by_nms)
+
+        with torch.autograd.profiler.record_function("uncrop_boxes_xyxy"):
+            # Return to the original image frame
+            data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box)
+        with torch.autograd.profiler.record_function("uncrop_points"):
+            data["points"] = uncrop_points(data["points"], crop_box)
+        with torch.autograd.profiler.record_function("crop_boxes"):
+            data["crop_boxes"] = torch.tensor(
+                [crop_box for _ in range(len(data["rles"]))]
+            )
+
+        return data
+
+    def _process_crop_batch(
+        self,
+        images: List[np.ndarray],
+        all_crop_boxes: List[List[int]],
+        all_layer_idxs: List[int],
+        all_orig_size_compact: List[Tuple[int, ...]],
+    ) -> List[MaskData]:
+        all_image = []
+        all_crop_box = []
+        all_layer_idx = []
+        all_orig_size = []
+        for image, orig_size, crop_boxes, layer_idxs in zip(
+            images, all_orig_size_compact, all_crop_boxes, all_layer_idxs
+        ):
+            # Iterate over image crops
+            for crop_box, layer_idx in zip(crop_boxes, layer_idxs):
+                all_image.append(image)
+                all_crop_box.append(crop_box)
+                all_layer_idx.append(layer_idx)
+                all_orig_size.append(orig_size)
+
+        # # TODO: NOTE: Calling process_crop in a loop like this might be an issue, because the predictor is stateful
+
+        all_cropped_im = [
+            crop_image(image, crop_box)
+            for image, crop_box in zip(all_image, all_crop_box)
+        ]
+
+        with torch.autograd.profiler.record_function("set_batch_image"):
+            self.predictor.set_image_batch(all_cropped_im)
+
+        i = 0
+        batch_features = self.predictor._features
+        all_crop_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 = get_image_size(cropped_im)
+            self.predictor._orig_hw = [get_image_size(cropped_im)]
+            self.predictor._features = {
+                "image_embed": batch_features["image_embed"][i].unsqueeze(0),
+                "high_res_feats": [
+                    b[i].unsqueeze(0) for b in batch_features["high_res_feats"]
+                ],
+            }
+            i += 1
+            self.predictor._is_image_set = True
+
+            # TODO: Batch mask_to_rle_pytorch_2 calls
+            # TODO: Specialize for rle-only return (specify which keys you want in data)
+
+            # all_crop_data.append(self._process_crop_points(cropped_im_size, layer_idx, crop_box, orig_size))
+
+            crop_layer_idx = layer_idx
+
+            # Get points for this crop
+            points_scale = np.array(cropped_im_size)[None, ::-1]
+            points_for_image = self.point_grids[crop_layer_idx] * points_scale
+
+            # Generate masks for this crop in batches
+            points_per_batch = self.points_per_batch
+            if self.points_per_batch is None:
+                points_per_batch = len(points_for_image)
+
+            all_batch_iterator_data = []
+            with torch.autograd.profiler.record_function("all _process_batch"):
+                for (points,) in batch_iterator(points_per_batch, points_for_image):
+                    # batch_data = self._process_batch(
+                    #     points, cropped_im_size, crop_box, orig_size, normalize=True
+                    # )
+
+                    im_size = cropped_im_size
+                    normalize = True
+
+                    orig_h, orig_w = orig_size
+
+                    orig_box = [0, 0, orig_w, orig_h]
+                    orig_box_torch = torch.as_tensor(
+                        orig_box, dtype=torch.float, device=self.predictor.device
+                    )
+                    crop_box_torch = torch.as_tensor(
+                        crop_box, dtype=torch.float, device=self.predictor.device
+                    )
+                    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()
+
+            result_data = None
+            with torch.autograd.profiler.record_function("all mask_to_rle_pytorch_2"):
+                for data in all_batch_iterator_data:
+                    # Compress to RLE
+                    data["masks"] = uncrop_masks(
+                        data["masks"], crop_box, orig_h, orig_w
+                    )
+                    # TODO: Capture all these masks in a single NT for mask_to_rle_pytorch_2
+                    # or at a minimum create a mask_to_rle_pytorch_2_list and use loops
+                    # to cause a single DtoH sync
+                    data["rles"] = _mask_to_rle_pytorch_2_0(data["masks"])
+                    del data["masks"]
+
+                    batch_data = data
+                    with torch.autograd.profiler.record_function("data.cat"):
+                        if result_data is None:
+                            result_data = batch_data
+                        else:
+                            result_data.cat(batch_data)
+                            del batch_data
+                self.predictor.reset_predictor()
+
+            all_crop_data.append(self._process_crop_points_dedup(result_data, crop_box))
+
+        i = 0
+        all_data = []
+        for _, _, crop_boxes, layer_idxs in zip(
+            images, all_orig_size, all_crop_boxes, all_layer_idxs
+        ):
+            data = None
+            for _, _ in zip(crop_boxes, layer_idxs):
+                if data is None:
+                    data = all_crop_data[i]
+                else:
+                    data.cat(all_crop_data[i])
+                i += 1
+            all_data.append(data)
+        return all_data
+
+    def _process_batch_fullgraph(
+        self,
+        points: np.ndarray,
+        im_size: Tuple[int, ...],
+        crop_box: List[int],
+        crop_box_torch: torch.Tensor,
+        orig_size: Tuple[int, ...],
+        normalize: bool,
+        orig_box_torch: torch.Tensor,
+    ) -> MaskData:
+        orig_h, orig_w = orig_size
+
+        # Run model on this batch
+        points = torch.as_tensor(points, dtype=torch.float32).pin_memory()
+        points = points.to(device=self.predictor.device, non_blocking=True)
+        in_points = self.predictor._transforms.transform_coords(
+            points, normalize=normalize, orig_hw=im_size
+        )
+        in_labels = torch.ones(
+            in_points.shape[0], dtype=torch.int, device=in_points.device
+        )
+        with torch.autograd.profiler.record_function("_predict"):
+            # NOTE: Just leaving this for reference. predict was split to
+            # allow earlier filtering by predicted iou
+            # masks, iou_preds, low_res_masks = self.predictor._predict(
+            masks = None
+            high_res_feats = self.predictor._features["high_res_feats"]
+            image_embed = self.predictor._features["image_embed"]
+            image_pe = self.predictor.model.sam_prompt_encoder.get_dense_pe().clone()
+            assert self.multimask_output, (
+                "Currently require multimask_output set to True"
+            )
+            high_res_feats_input = [
+                feat_level[-1].unsqueeze(0).clone()
+                # for feat_level in self._features["high_res_feats"]
+                for feat_level in high_res_feats
+            ]
+            image_embed_input = image_embed[-1].unsqueeze(0).clone()
+            low_res_masks, iou_preds = self.predictor._predict_masks(
+                [t.contiguous() for t in high_res_feats_input],
+                image_embed_input.contiguous(),
+                image_pe.contiguous(),
+                in_points[:, None, :].contiguous(),
+                in_labels[:, None].contiguous(),
+                boxes=None,
+                mask_input=None,
+                multimask_output=self.multimask_output,
+                # img_idx=-1,
+            )
+
+        x0, y0, _, _ = crop_box
+        points = points.repeat_interleave(3 if masks is None else masks.shape[1], dim=0)
+
+        if not self.use_m2m:
+            with torch.autograd.profiler.record_function("thresh and filter"):
+                # Filter by predicted IoU
+                if self.pred_iou_thresh > 0.0:
+                    keep_mask = iou_preds.flatten(0, 1) > self.pred_iou_thresh
+                    keep_index = keep_mask.nonzero(as_tuple=True)[0]
+                    low_res_masks = low_res_masks.flatten(0, 1).unsqueeze(1)
+                    low_res_masks = low_res_masks[keep_index]
+                    iou_preds = iou_preds.flatten(0, 1).unsqueeze(1)[keep_index]
+                    points = points[keep_index]
+        if masks is None:
+            masks, low_res_mask = self.predictor._predict_masks_postprocess(
+                low_res_masks, -1, True, channel_1=low_res_masks.size(1) == 1
+            )
+
+        # Serialize predictions and store in MaskData
+        with torch.autograd.profiler.record_function("MaskData"):
+            data = MaskData(
+                masks=masks.flatten(0, 1),
+                iou_preds=iou_preds.flatten(0, 1),
+                points=points,
+                low_res_masks=low_res_masks.flatten(0, 1),
+            )
+        del masks
+
+        keep_mask = None
+
+        if not self.use_m2m:
+            # NOTE: This is left just for reference. We filter earlier for this case to save
+            # on compute within the expensive _predict_masks_postprocess
+            # with torch.autograd.profiler.record_function("thresh and filter"):
+            #     # Filter by predicted IoU
+            #     if self.pred_iou_thresh > 0.0:
+            #         keep_mask = data["iou_preds"] > self.pred_iou_thresh
+            #         # TODO: Might need this for correctness due to calculate_stability_score IoU?
+            #         data.filter(keep_mask)
+
+            with torch.autograd.profiler.record_function("calculate_stability_score"):
+                # Calculate and filter by stability score
+                data["stability_score"] = self.calculate_stability_score(
+                    data["masks"], self.mask_threshold, self.stability_score_offset
+                )
+            with torch.autograd.profiler.record_function("stability_score_thresh"):
+                if self.stability_score_thresh > 0.0:
+                    keep_mask = data["stability_score"] >= self.stability_score_thresh
+                    keep_index = keep_mask.nonzero(as_tuple=True)[0]
+                    data.filter(keep_index)
+        else:
+            # One step refinement using previous mask predictions
+            in_points = self.predictor._transforms.transform_coords(
+                data["points"], normalize=normalize, orig_hw=im_size
+            )
+            labels = torch.ones(
+                in_points.shape[0], dtype=torch.int, device=in_points.device
+            )
+            masks, ious = self.refine_with_m2m(
+                in_points, labels, data["low_res_masks"], self.points_per_batch
+            )
+            data["masks"] = masks.squeeze(1)
+            data["iou_preds"] = ious.squeeze(1)
+
+            if self.pred_iou_thresh > 0.0:
+                keep_mask = data["iou_preds"] > self.pred_iou_thresh
+                data.filter(keep_mask)
+
+            data["stability_score"] = self.calculate_stability_score(
+                data["masks"], self.mask_threshold, self.stability_score_offset
+            )
+            if self.stability_score_thresh > 0.0:
+                keep_mask = data["stability_score"] >= self.stability_score_thresh
+                data.filter(keep_mask)
+
+        with torch.autograd.profiler.record_function(
+            "Threshold masks and calculate boxes"
+        ):
+            # Threshold masks and calculate boxes
+            data["masks"] = data["masks"] > self.mask_threshold
+            data["boxes"] = self.batched_mask_to_box(data["masks"])
+
+        with torch.autograd.profiler.record_function("is_box_near_crop_edge"):
+            # Filter boxes that touch crop boundaries
+            keep_mask = ~is_box_near_crop_edge_torch(
+                data["boxes"],
+                crop_box,
+                crop_box_torch,
+                orig_box_torch,
+            )
+
+        with torch.autograd.profiler.record_function("filter(keep_mask)"):
+            keep_index = keep_mask.nonzero(as_tuple=True)[0]
+            data.filter(keep_index)
+
+        return data
+
+    def _process_batch(
+        self,
+        points: np.ndarray,
+        im_size: Tuple[int, ...],
+        crop_box: List[int],
+        orig_size: Tuple[int, ...],
+        normalize=False,
+    ) -> MaskData:
+        orig_h, orig_w = orig_size
+
+        orig_box = [0, 0, orig_w, orig_h]
+        orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float)
+        orig_box_torch = orig_box_torch.pin_memory()
+        orig_box_torch = orig_box_torch.to(
+            device=self.predictor.device, non_blocking=True
+        )
+
+        crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float)
+        crop_box_torch = crop_box_torch.pin_memory()
+        crop_box_torch = crop_box_torch.to(
+            device=self.predictor.device, non_blocking=True
+        )
+
+        data = self._process_batch_fullgraph(
+            points,
+            im_size,
+            crop_box,
+            crop_box_torch,
+            orig_size,
+            normalize,
+            orig_box_torch,
+        )
+
+        with torch.autograd.profiler.record_function("uncrop_masks"):
+            # Compress to RLE
+            data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w)
+            data["rles"] = _mask_to_rle_pytorch_2_0(data["masks"])
+            del data["masks"]
+
+        return data
+
+    @staticmethod
+    def postprocess_small_regions(
+        mask_data: MaskData, min_area: int, nms_thresh: float
+    ) -> MaskData:
+        """
+        Removes small disconnected regions and holes in masks, then reruns
+        box NMS to remove any new duplicates.
+
+        Edits mask_data in place.
+
+        Requires open-cv as a dependency.
+        """
+        if len(mask_data["rles"]) == 0:
+            return mask_data
+
+        # Filter small disconnected regions and holes
+        new_masks = []
+        scores = []
+        for rle in mask_data["rles"]:
+            mask = rle_to_mask(rle)
+
+            mask, changed = remove_small_regions(mask, min_area, mode="holes")
+            unchanged = not changed
+            mask, changed = remove_small_regions(mask, min_area, mode="islands")
+            unchanged = unchanged and not changed
+
+            new_masks.append(torch.as_tensor(mask).unsqueeze(0))
+            # Give score=0 to changed masks and score=1 to unchanged masks
+            # so NMS will prefer ones that didn't need postprocessing
+            scores.append(float(unchanged))
+
+        # Recalculate boxes and remove any new duplicates
+        masks = torch.cat(new_masks, dim=0)
+        # TODO: This doesn't use the possibly compiled self.batched_mask_to_box
+        boxes = batched_mask_to_box(masks)
+        keep_by_nms = batched_nms(
+            boxes.float(),
+            torch.as_tensor(scores),
+            torch.zeros_like(boxes[:, 0]),  # categories
+            iou_threshold=nms_thresh,
+        )
+
+        # Only recalculate RLEs for masks that have changed
+        for i_mask in keep_by_nms:
+            if scores[i_mask] == 0.0:
+                mask_torch = masks[i_mask].unsqueeze(0)
+                mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]
+                mask_data["boxes"][i_mask] = boxes[i_mask]  # update res directly
+        mask_data.filter(keep_by_nms)
+
+        return mask_data
+
+    def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch):
+        new_masks = []
+        new_iou_preds = []
+
+        for cur_points, cur_point_labels, low_res_mask in batch_iterator(
+            points_per_batch, points, point_labels, low_res_masks
+        ):
+            best_masks, best_iou_preds, _ = self.predictor._predict(
+                cur_points[:, None, :],
+                cur_point_labels[:, None],
+                mask_input=low_res_mask[:, None, :],
+                multimask_output=False,
+                return_logits=True,
+            )
+            new_masks.append(best_masks)
+            new_iou_preds.append(best_iou_preds)
+        masks = torch.cat(new_masks, dim=0)
+        return masks, torch.cat(new_iou_preds, dim=0)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/build_sam.py

@@ -0,0 +1,166 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+
+import torch
+from hydra import compose
+from hydra.utils import instantiate
+from omegaconf import OmegaConf
+
+from torchao._models import sam2
+
+# Check if the user is running Python from the parent directory of the sam2 repo
+# (i.e. the directory where this repo is cloned into) -- this is not supported since
+# it could shadow the sam2 package and cause issues.
+if os.path.isdir(os.path.join(sam2.__path__[0], "sam2")):
+    # If the user has "sam2/sam2" in their path, they are likey importing the repo itself
+    # as "sam2" rather than importing the "sam2" python package (i.e. "sam2/sam2" directory).
+    # This typically happens because the user is running Python from the parent directory
+    # that contains the sam2 repo they cloned.
+    raise RuntimeError(
+        "You're likely running Python from the parent directory of the sam2 repository "
+        "(i.e. the directory where https://github.com/facebookresearch/sam2 is cloned into). "
+        "This is not supported since the `sam2` Python package could be shadowed by the "
+        "repository name (the repository is also named `sam2` and contains the Python package "
+        "in `sam2/sam2`). Please run Python from another directory (e.g. from the repo dir "
+        "rather than its parent dir, or from your home directory) after installing SAM 2."
+    )
+
+
+HF_MODEL_ID_TO_FILENAMES = {
+    "facebook/sam2-hiera-tiny": (
+        "configs/sam2/sam2_hiera_t.yaml",
+        "sam2_hiera_tiny.pt",
+    ),
+    "facebook/sam2-hiera-small": (
+        "configs/sam2/sam2_hiera_s.yaml",
+        "sam2_hiera_small.pt",
+    ),
+    "facebook/sam2-hiera-base-plus": (
+        "configs/sam2/sam2_hiera_b+.yaml",
+        "sam2_hiera_base_plus.pt",
+    ),
+    "facebook/sam2-hiera-large": (
+        "configs/sam2/sam2_hiera_l.yaml",
+        "sam2_hiera_large.pt",
+    ),
+    "facebook/sam2.1-hiera-tiny": (
+        "configs/sam2.1/sam2.1_hiera_t.yaml",
+        "sam2.1_hiera_tiny.pt",
+    ),
+    "facebook/sam2.1-hiera-small": (
+        "configs/sam2.1/sam2.1_hiera_s.yaml",
+        "sam2.1_hiera_small.pt",
+    ),
+    "facebook/sam2.1-hiera-base-plus": (
+        "configs/sam2.1/sam2.1_hiera_b+.yaml",
+        "sam2.1_hiera_base_plus.pt",
+    ),
+    "facebook/sam2.1-hiera-large": (
+        "configs/sam2.1/sam2.1_hiera_l.yaml",
+        "sam2.1_hiera_large.pt",
+    ),
+}
+
+
+def build_sam2(
+    config_file,
+    ckpt_path=None,
+    device="cuda",
+    mode="eval",
+    hydra_overrides_extra=[],
+    apply_postprocessing=True,
+    **kwargs,
+):
+    if apply_postprocessing:
+        hydra_overrides_extra = hydra_overrides_extra.copy()
+        hydra_overrides_extra += [
+            # dynamically fall back to multi-mask if the single mask is not stable
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
+        ]
+    # Read config and init model
+    cfg = compose(config_name=config_file, overrides=hydra_overrides_extra)
+    OmegaConf.resolve(cfg)
+    model = instantiate(cfg.model, _recursive_=True)
+    _load_checkpoint(model, ckpt_path)
+    model = model.to(device)
+    if mode == "eval":
+        model.eval()
+    return model
+
+
+def build_sam2_video_predictor(
+    config_file,
+    ckpt_path=None,
+    device="cuda",
+    mode="eval",
+    hydra_overrides_extra=[],
+    apply_postprocessing=True,
+    **kwargs,
+):
+    hydra_overrides = [
+        "++model._target_=torchao._models.sam2.sam2_video_predictor.SAM2VideoPredictor",
+    ]
+    if apply_postprocessing:
+        hydra_overrides_extra = hydra_overrides_extra.copy()
+        hydra_overrides_extra += [
+            # dynamically fall back to multi-mask if the single mask is not stable
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
+            "++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
+            # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking
+            "++model.binarize_mask_from_pts_for_mem_enc=true",
+            # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)
+            "++model.fill_hole_area=8",
+        ]
+    hydra_overrides.extend(hydra_overrides_extra)
+
+    # Read config and init model
+    cfg = compose(config_name=config_file, overrides=hydra_overrides)
+    OmegaConf.resolve(cfg)
+    model = instantiate(cfg.model, _recursive_=True)
+    _load_checkpoint(model, ckpt_path)
+    model = model.to(device)
+    if mode == "eval":
+        model.eval()
+    return model
+
+
+def _hf_download(model_id):
+    from huggingface_hub import hf_hub_download
+
+    config_name, checkpoint_name = HF_MODEL_ID_TO_FILENAMES[model_id]
+    ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)
+    return config_name, ckpt_path
+
+
+def build_sam2_hf(model_id, **kwargs):
+    config_name, ckpt_path = _hf_download(model_id)
+    return build_sam2(config_file=config_name, ckpt_path=ckpt_path, **kwargs)
+
+
+def build_sam2_video_predictor_hf(model_id, **kwargs):
+    config_name, ckpt_path = _hf_download(model_id)
+    return build_sam2_video_predictor(
+        config_file=config_name, ckpt_path=ckpt_path, **kwargs
+    )
+
+
+def _load_checkpoint(model, ckpt_path):
+    if ckpt_path is not None:
+        sd = torch.load(ckpt_path, map_location="cpu", weights_only=True)["model"]
+        missing_keys, unexpected_keys = model.load_state_dict(sd)
+        if missing_keys:
+            logging.error(missing_keys)
+            raise RuntimeError()
+        if unexpected_keys:
+            logging.error(unexpected_keys)
+            raise RuntimeError()
+        logging.info("Loaded checkpoint sucessfully")

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/configs/sam2.1/sam2.1_hiera_l.yaml

@@ -0,0 +1,120 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 144
+      num_heads: 2
+      stages: [2, 6, 36, 4]
+      global_att_blocks: [23, 33, 43]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+      window_spec: [8, 4, 16, 8]
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [1152, 576, 288, 144]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  no_obj_embed_spatial: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: true
+  proj_tpos_enc_in_obj_ptrs: true
+  use_signed_tpos_enc_to_obj_ptrs: true
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/configs/sam2.1/sam2.1_hiera_t.yaml

@@ -0,0 +1,121 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 96
+      num_heads: 1
+      stages: [1, 2, 7, 2]
+      global_att_blocks: [5, 7, 9]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [768, 384, 192, 96]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  # SAM decoder
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  no_obj_embed_spatial: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: true
+  proj_tpos_enc_in_obj_ptrs: true
+  use_signed_tpos_enc_to_obj_ptrs: true
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  # HieraT does not currently support compilation, should always be set to False
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml

@@ -0,0 +1,339 @@
+# @package _global_
+
+scratch:
+  resolution: 1024
+  train_batch_size: 1
+  num_train_workers: 10
+  num_frames: 8
+  max_num_objects: 3
+  base_lr: 5.0e-6
+  vision_lr: 3.0e-06
+  phases_per_epoch: 1
+  num_epochs: 40
+
+dataset:
+  # PATHS to Dataset
+  img_folder: null # PATH to MOSE JPEGImages folder
+  gt_folder: null  # PATH to MOSE Annotations folder
+  file_list_txt: training/assets/MOSE_sample_train_list.txt # Optional PATH to filelist containing a subset of videos to be used for training
+  multiplier: 2
+
+# Video transforms
+vos:
+  train_transforms:
+    - _target_: training.dataset.transforms.ComposeAPI
+      transforms:
+        - _target_: training.dataset.transforms.RandomHorizontalFlip
+          consistent_transform: True
+        - _target_: training.dataset.transforms.RandomAffine
+          degrees: 25
+          shear: 20
+          image_interpolation: bilinear
+          consistent_transform: True
+        - _target_: training.dataset.transforms.RandomResizeAPI
+          sizes: ${scratch.resolution}
+          square: true
+          consistent_transform: True
+        - _target_: training.dataset.transforms.ColorJitter
+          consistent_transform: True
+          brightness: 0.1
+          contrast: 0.03
+          saturation: 0.03
+          hue: null
+        - _target_: training.dataset.transforms.RandomGrayscale
+          p: 0.05
+          consistent_transform: True
+        - _target_: training.dataset.transforms.ColorJitter
+          consistent_transform: False
+          brightness: 0.1
+          contrast: 0.05
+          saturation: 0.05
+          hue: null
+        - _target_: training.dataset.transforms.ToTensorAPI
+        - _target_: training.dataset.transforms.NormalizeAPI
+          mean: [0.485, 0.456, 0.406]
+          std: [0.229, 0.224, 0.225]
+
+trainer:
+  _target_: training.trainer.Trainer
+  mode: train_only
+  max_epochs: ${times:${scratch.num_epochs},${scratch.phases_per_epoch}}
+  accelerator: cuda
+  seed_value: 123
+
+  model:
+    _target_: training.model.sam2.SAM2Train
+    image_encoder:
+      _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
+      scalp: 1
+      trunk:
+        _target_: sam2.modeling.backbones.hieradet.Hiera
+        embed_dim: 112
+        num_heads: 2
+        drop_path_rate: 0.1
+      neck:
+        _target_: sam2.modeling.backbones.image_encoder.FpnNeck
+        position_encoding:
+          _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+          num_pos_feats: 256
+          normalize: true
+          scale: null
+          temperature: 10000
+        d_model: 256
+        backbone_channel_list: [896, 448, 224, 112]
+        fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+        fpn_interp_model: nearest
+
+    memory_attention:
+      _target_: sam2.modeling.memory_attention.MemoryAttention
+      d_model: 256
+      pos_enc_at_input: true
+      layer:
+        _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
+        activation: relu
+        dim_feedforward: 2048
+        dropout: 0.1
+        pos_enc_at_attn: false
+        self_attention:
+          _target_: sam2.modeling.sam.transformer.RoPEAttention
+          rope_theta: 10000.0
+          feat_sizes: [32, 32]
+          embedding_dim: 256
+          num_heads: 1
+          downsample_rate: 1
+          dropout: 0.1
+        d_model: 256
+        pos_enc_at_cross_attn_keys: true
+        pos_enc_at_cross_attn_queries: false
+        cross_attention:
+          _target_: sam2.modeling.sam.transformer.RoPEAttention
+          rope_theta: 10000.0
+          feat_sizes: [32, 32]
+          rope_k_repeat: True
+          embedding_dim: 256
+          num_heads: 1
+          downsample_rate: 1
+          dropout: 0.1
+          kv_in_dim: 64
+      num_layers: 4
+
+    memory_encoder:
+        _target_: sam2.modeling.memory_encoder.MemoryEncoder
+        out_dim: 64
+        position_encoding:
+          _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
+          num_pos_feats: 64
+          normalize: true
+          scale: null
+          temperature: 10000
+        mask_downsampler:
+          _target_: sam2.modeling.memory_encoder.MaskDownSampler
+          kernel_size: 3
+          stride: 2
+          padding: 1
+        fuser:
+          _target_: sam2.modeling.memory_encoder.Fuser
+          layer:
+            _target_: sam2.modeling.memory_encoder.CXBlock
+            dim: 256
+            kernel_size: 7
+            padding: 3
+            layer_scale_init_value: 1e-6
+            use_dwconv: True  # depth-wise convs
+          num_layers: 2
+
+    num_maskmem: 7
+    image_size: ${scratch.resolution}
+    # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+    sigmoid_scale_for_mem_enc: 20.0
+    sigmoid_bias_for_mem_enc: -10.0
+    use_mask_input_as_output_without_sam: true
+    # Memory
+    directly_add_no_mem_embed: true
+    no_obj_embed_spatial: true
+    # use high-resolution feature map in the SAM mask decoder
+    use_high_res_features_in_sam: true
+    # output 3 masks on the first click on initial conditioning frames
+    multimask_output_in_sam: true
+    # SAM heads
+    iou_prediction_use_sigmoid: True
+    # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+    use_obj_ptrs_in_encoder: true
+    add_tpos_enc_to_obj_ptrs: true
+    proj_tpos_enc_in_obj_ptrs: true
+    use_signed_tpos_enc_to_obj_ptrs: true
+    only_obj_ptrs_in_the_past_for_eval: true
+    # object occlusion prediction
+    pred_obj_scores: true
+    pred_obj_scores_mlp: true
+    fixed_no_obj_ptr: true
+    # multimask tracking settings
+    multimask_output_for_tracking: true
+    use_multimask_token_for_obj_ptr: true
+    multimask_min_pt_num: 0
+    multimask_max_pt_num: 1
+    use_mlp_for_obj_ptr_proj: true
+    # Compilation flag
+    # compile_image_encoder: False
+
+    ####### Training specific params #######
+    # box/point input and corrections
+    prob_to_use_pt_input_for_train: 0.5
+    prob_to_use_pt_input_for_eval: 0.0
+    prob_to_use_box_input_for_train: 0.5  # 0.5*0.5 = 0.25 prob to use box instead of points
+    prob_to_use_box_input_for_eval: 0.0
+    prob_to_sample_from_gt_for_train: 0.1  # with a small prob, sampling correction points from GT mask instead of prediction errors
+    num_frames_to_correct_for_train: 2  # iteratively sample on random 1~2 frames (always include the first frame)
+    num_frames_to_correct_for_eval: 1  # only iteratively sample on first frame
+    rand_frames_to_correct_for_train: True  # random #init-cond-frame ~ 2
+    add_all_frames_to_correct_as_cond: True  # when a frame receives a correction click, it becomes a conditioning frame (even if it's not initially a conditioning frame)
+    # maximum 2 initial conditioning frames
+    num_init_cond_frames_for_train: 2
+    rand_init_cond_frames_for_train: True  # random 1~2
+    num_correction_pt_per_frame: 7
+    use_act_ckpt_iterative_pt_sampling: false
+    
+
+    
+    num_init_cond_frames_for_eval: 1  # only mask on the first frame
+    forward_backbone_per_frame_for_eval: True
+    
+
+  data:
+    train:
+      _target_: training.dataset.sam2_datasets.TorchTrainMixedDataset
+      phases_per_epoch: ${scratch.phases_per_epoch}
+      batch_sizes:
+        - ${scratch.train_batch_size}
+
+      datasets:
+        - _target_: training.dataset.utils.RepeatFactorWrapper
+          dataset:
+            _target_: training.dataset.utils.ConcatDataset
+            datasets:
+            - _target_: training.dataset.vos_dataset.VOSDataset
+              transforms: ${vos.train_transforms}
+              training: true
+              video_dataset:
+                _target_: training.dataset.vos_raw_dataset.PNGRawDataset
+                img_folder: ${dataset.img_folder}
+                gt_folder: ${dataset.gt_folder}
+                file_list_txt: ${dataset.file_list_txt}
+              sampler:
+                _target_: training.dataset.vos_sampler.RandomUniformSampler
+                num_frames: ${scratch.num_frames}
+                max_num_objects: ${scratch.max_num_objects}
+              multiplier: ${dataset.multiplier}
+      shuffle: True
+      num_workers: ${scratch.num_train_workers}
+      pin_memory: True
+      drop_last: True
+      collate_fn:
+        _target_: training.utils.data_utils.collate_fn
+        _partial_: true
+        dict_key: all
+
+  optim:
+    amp:
+      enabled: True
+      amp_dtype: bfloat16
+
+    optimizer:
+      _target_: torch.optim.AdamW
+
+    gradient_clip:
+      _target_: training.optimizer.GradientClipper
+      max_norm: 0.1
+      norm_type: 2
+
+    param_group_modifiers:
+      - _target_: training.optimizer.layer_decay_param_modifier
+        _partial_: True
+        layer_decay_value: 0.9
+        apply_to: 'image_encoder.trunk'
+        overrides:
+          - pattern: '*pos_embed*'
+            value: 1.0
+
+    options:
+      lr:
+        - scheduler:
+            _target_: fvcore.common.param_scheduler.CosineParamScheduler
+            start_value: ${scratch.base_lr}
+            end_value: ${divide:${scratch.base_lr},10}
+        - scheduler:
+            _target_: fvcore.common.param_scheduler.CosineParamScheduler
+            start_value: ${scratch.vision_lr}
+            end_value: ${divide:${scratch.vision_lr},10}
+          param_names:
+            - 'image_encoder.*'
+      weight_decay:
+        - scheduler:
+            _target_: fvcore.common.param_scheduler.ConstantParamScheduler
+            value: 0.1
+        - scheduler:
+            _target_: fvcore.common.param_scheduler.ConstantParamScheduler
+            value: 0.0
+          param_names:
+            - '*bias*'
+          module_cls_names: ['torch.nn.LayerNorm']
+
+  loss:
+    all:
+      _target_: training.loss_fns.MultiStepMultiMasksAndIous
+      weight_dict:
+        loss_mask: 20
+        loss_dice: 1
+        loss_iou: 1
+        loss_class: 1
+      supervise_all_iou: true
+      iou_use_l1_loss: true
+      pred_obj_scores: true
+      focal_gamma_obj_score: 0.0
+      focal_alpha_obj_score: -1.0
+
+  distributed:
+    backend: nccl
+    find_unused_parameters: True
+
+  logging:
+    tensorboard_writer:
+      _target_: training.utils.logger.make_tensorboard_logger
+      log_dir:  ${launcher.experiment_log_dir}/tensorboard
+      flush_secs: 120
+      should_log: True
+    log_dir: ${launcher.experiment_log_dir}/logs
+    log_freq: 10
+
+  # initialize from a SAM 2 checkpoint
+  checkpoint:
+    save_dir: ${launcher.experiment_log_dir}/checkpoints
+    save_freq: 0 # 0 only last checkpoint is saved.
+    model_weight_initializer:
+      _partial_: True
+      _target_: training.utils.checkpoint_utils.load_state_dict_into_model
+      strict: True
+      ignore_unexpected_keys: null
+      ignore_missing_keys: null
+
+      state_dict:
+        _target_: training.utils.checkpoint_utils.load_checkpoint_and_apply_kernels
+        checkpoint_path: ./checkpoints/sam2.1_hiera_base_plus.pt # PATH to SAM 2.1 checkpoint
+        ckpt_state_dict_keys: ['model']
+
+launcher:
+  num_nodes: 1
+  gpus_per_node: 8
+  experiment_log_dir: null # Path to log directory, defaults to ./sam2_logs/${config_name}
+
+# SLURM args if running on a cluster
+submitit:
+  partition: null
+  account: null
+  qos: null
+  cpus_per_task: 10
+  use_cluster: false
+  timeout_hour: 24
+  name: null
+  port_range: [10000, 65000]
+

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/csrc/connected_components.cu

@@ -0,0 +1,289 @@
+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+// adapted from https://github.com/zsef123/Connected_components_PyTorch
+// with license found in the LICENSE_cctorch file in the root directory.
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <torch/extension.h>
+#include <torch/script.h>
+#include <vector>
+
+// 2d
+#define BLOCK_ROWS 16
+#define BLOCK_COLS 16
+
+namespace cc2d {
+
+template <typename T>
+__device__ __forceinline__ unsigned char hasBit(T bitmap, unsigned char pos) {
+  return (bitmap >> pos) & 1;
+}
+
+__device__ int32_t find(const int32_t* s_buf, int32_t n) {
+  while (s_buf[n] != n)
+    n = s_buf[n];
+  return n;
+}
+
+__device__ int32_t find_n_compress(int32_t* s_buf, int32_t n) {
+  const int32_t id = n;
+  while (s_buf[n] != n) {
+    n = s_buf[n];
+    s_buf[id] = n;
+  }
+  return n;
+}
+
+__device__ void union_(int32_t* s_buf, int32_t a, int32_t b) {
+  bool done;
+  do {
+    a = find(s_buf, a);
+    b = find(s_buf, b);
+
+    if (a < b) {
+      int32_t old = atomicMin(s_buf + b, a);
+      done = (old == b);
+      b = old;
+    } else if (b < a) {
+      int32_t old = atomicMin(s_buf + a, b);
+      done = (old == a);
+      a = old;
+    } else
+      done = true;
+
+  } while (!done);
+}
+
+__global__ void
+init_labeling(int32_t* label, const uint32_t W, const uint32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+  const uint32_t idx = row * W + col;
+
+  if (row < H && col < W)
+    label[idx] = idx;
+}
+
+__global__ void
+merge(uint8_t* img, int32_t* label, const uint32_t W, const uint32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+  const uint32_t idx = row * W + col;
+
+  if (row >= H || col >= W)
+    return;
+
+  uint32_t P = 0;
+
+  if (img[idx])
+    P |= 0x777;
+  if (row + 1 < H && img[idx + W])
+    P |= 0x777 << 4;
+  if (col + 1 < W && img[idx + 1])
+    P |= 0x777 << 1;
+
+  if (col == 0)
+    P &= 0xEEEE;
+  if (col + 1 >= W)
+    P &= 0x3333;
+  else if (col + 2 >= W)
+    P &= 0x7777;
+
+  if (row == 0)
+    P &= 0xFFF0;
+  if (row + 1 >= H)
+    P &= 0xFF;
+
+  if (P > 0) {
+    // If need check about top-left pixel(if flag the first bit) and hit the
+    // top-left pixel
+    if (hasBit(P, 0) && img[idx - W - 1]) {
+      union_(label, idx, idx - 2 * W - 2); // top left block
+    }
+
+    if ((hasBit(P, 1) && img[idx - W]) || (hasBit(P, 2) && img[idx - W + 1]))
+      union_(label, idx, idx - 2 * W); // top bottom block
+
+    if (hasBit(P, 3) && img[idx + 2 - W])
+      union_(label, idx, idx - 2 * W + 2); // top right block
+
+    if ((hasBit(P, 4) && img[idx - 1]) || (hasBit(P, 8) && img[idx + W - 1]))
+      union_(label, idx, idx - 2); // just left block
+  }
+}
+
+__global__ void compression(int32_t* label, const int32_t W, const int32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+  const uint32_t idx = row * W + col;
+
+  if (row < H && col < W)
+    find_n_compress(label, idx);
+}
+
+__global__ void final_labeling(
+    const uint8_t* img,
+    int32_t* label,
+    const int32_t W,
+    const int32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
+  const uint32_t idx = row * W + col;
+
+  if (row >= H || col >= W)
+    return;
+
+  int32_t y = label[idx] + 1;
+
+  if (img[idx])
+    label[idx] = y;
+  else
+    label[idx] = 0;
+
+  if (col + 1 < W) {
+    if (img[idx + 1])
+      label[idx + 1] = y;
+    else
+      label[idx + 1] = 0;
+
+    if (row + 1 < H) {
+      if (img[idx + W + 1])
+        label[idx + W + 1] = y;
+      else
+        label[idx + W + 1] = 0;
+    }
+  }
+
+  if (row + 1 < H) {
+    if (img[idx + W])
+      label[idx + W] = y;
+    else
+      label[idx + W] = 0;
+  }
+}
+
+__global__ void init_counting(
+    const int32_t* label,
+    int32_t* count_init,
+    const int32_t W,
+    const int32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);
+  const uint32_t idx = row * W + col;
+
+  if (row >= H || col >= W)
+    return;
+
+  int32_t y = label[idx];
+  if (y > 0) {
+    int32_t count_idx = y - 1;
+    atomicAdd(count_init + count_idx, 1);
+  }
+}
+
+__global__ void final_counting(
+    const int32_t* label,
+    const int32_t* count_init,
+    int32_t* count_final,
+    const int32_t W,
+    const int32_t H) {
+  const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);
+  const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);
+  const uint32_t idx = row * W + col;
+
+  if (row >= H || col >= W)
+    return;
+
+  int32_t y = label[idx];
+  if (y > 0) {
+    int32_t count_idx = y - 1;
+    count_final[idx] = count_init[count_idx];
+  } else {
+    count_final[idx] = 0;
+  }
+}
+
+} // namespace cc2d
+
+std::vector<torch::Tensor> get_connected_componnets(
+    const torch::Tensor& inputs) {
+  AT_ASSERTM(inputs.is_cuda(), "inputs must be a CUDA tensor");
+  AT_ASSERTM(inputs.ndimension() == 4, "inputs must be [N, 1, H, W] shape");
+  AT_ASSERTM(
+      inputs.scalar_type() == torch::kUInt8, "inputs must be a uint8 type");
+
+  const uint32_t N = inputs.size(0);
+  const uint32_t C = inputs.size(1);
+  const uint32_t H = inputs.size(2);
+  const uint32_t W = inputs.size(3);
+
+  AT_ASSERTM(C == 1, "inputs must be [N, 1, H, W] shape");
+  AT_ASSERTM((H % 2) == 0, "height must be an even number");
+  AT_ASSERTM((W % 2) == 0, "width must be an even number");
+
+  // label must be uint32_t
+  auto label_options =
+      torch::TensorOptions().dtype(torch::kInt32).device(inputs.device());
+  torch::Tensor labels = torch::zeros({N, C, H, W}, label_options);
+  torch::Tensor counts_init = torch::zeros({N, C, H, W}, label_options);
+  torch::Tensor counts_final = torch::zeros({N, C, H, W}, label_options);
+
+  dim3 grid = dim3(
+      ((W + 1) / 2 + BLOCK_COLS - 1) / BLOCK_COLS,
+      ((H + 1) / 2 + BLOCK_ROWS - 1) / BLOCK_ROWS);
+  dim3 block = dim3(BLOCK_COLS, BLOCK_ROWS);
+  dim3 grid_count =
+      dim3((W + BLOCK_COLS) / BLOCK_COLS, (H + BLOCK_ROWS) / BLOCK_ROWS);
+  dim3 block_count = dim3(BLOCK_COLS, BLOCK_ROWS);
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  for (int n = 0; n < N; n++) {
+    uint32_t offset = n * H * W;
+
+    cc2d::init_labeling<<<grid, block, 0, stream>>>(
+        labels.data_ptr<int32_t>() + offset, W, H);
+    cc2d::merge<<<grid, block, 0, stream>>>(
+        inputs.data_ptr<uint8_t>() + offset,
+        labels.data_ptr<int32_t>() + offset,
+        W,
+        H);
+    cc2d::compression<<<grid, block, 0, stream>>>(
+        labels.data_ptr<int32_t>() + offset, W, H);
+    cc2d::final_labeling<<<grid, block, 0, stream>>>(
+        inputs.data_ptr<uint8_t>() + offset,
+        labels.data_ptr<int32_t>() + offset,
+        W,
+        H);
+
+    // get the counting of each pixel
+    cc2d::init_counting<<<grid_count, block_count, 0, stream>>>(
+        labels.data_ptr<int32_t>() + offset,
+        counts_init.data_ptr<int32_t>() + offset,
+        W,
+        H);
+    cc2d::final_counting<<<grid_count, block_count, 0, stream>>>(
+        labels.data_ptr<int32_t>() + offset,
+        counts_init.data_ptr<int32_t>() + offset,
+        counts_final.data_ptr<int32_t>() + offset,
+        W,
+        H);
+  }
+
+  // returned values are [labels, counts]
+  std::vector<torch::Tensor> outputs;
+  outputs.push_back(labels);
+  outputs.push_back(counts_final);
+  return outputs;
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def(
+      "get_connected_componnets",
+      &get_connected_componnets,
+      "get_connected_componnets");
+}

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/map_tensor.py

@@ -0,0 +1,781 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD 3-Clause license found in the
+# LICENSE file in the root directory of this source tree.
+import contextlib
+import functools
+from typing import Dict
+
+import torch
+from torch.nested._internal.nested_tensor import nested_view_from_values_offsets
+from torch.utils._pytree import tree_map
+
+MAP_TENSOR_ATEN_OP_TABLE = {}
+
+
+def implements(aten_ops_or_torch_fns):
+    if not isinstance(aten_ops_or_torch_fns, (list, tuple)):
+        aten_ops_or_torch_fns = [aten_ops_or_torch_fns]
+
+    def decorator(func):
+        for op in aten_ops_or_torch_fns:
+
+            @functools.wraps(op)
+            def wrapper(f, types, args, kwargs):
+                return func(f, types, args, kwargs)
+
+            MAP_TENSOR_ATEN_OP_TABLE[op] = wrapper
+        return func
+
+    return decorator
+
+
+@contextlib.contextmanager
+def no_dispatch():
+    guard = torch._C._DisableTorchDispatch()
+    try:
+        yield
+    finally:
+        del guard
+
+
+def wrap_dim(i, dim):
+    if i < 0:
+        return dim + i
+    return i
+
+
+def unwrap(t):
+    if isinstance(t, MapTensor):
+        with no_dispatch():
+            return t.elems
+    else:
+        return t
+
+
+def unwrap_i(t, i):
+    if isinstance(t, MapTensor):
+        with no_dispatch():
+            return t.elems[i]
+    else:
+        return t
+
+
+def unwrap_fn(t, fn):
+    if isinstance(t, MapTensor):
+        with no_dispatch():
+            return fn(t.elems)
+    else:
+        return None
+
+
+def wrap(t):
+    if isinstance(t, torch.Tensor):
+        return MapTensor(t)
+    else:
+        return t
+
+
+@implements(torch.ops.aten.native_layer_norm.default)
+def layer_norm_impl(func, types, args, kwargs=None):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 5, f"args: {unwrapped_args}"
+    norm_res = func(*unwrapped_args)
+    assert len(norm_res) == 3
+    return tuple(wrap(a) for a in norm_res)
+
+
+@implements(torch.ops.aten.add.Tensor)
+def add_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    if not isinstance(args[0], MapTensor) and isinstance(args[1], MapTensor):
+        if args[0].dim() == (args[1].dim() + 1):
+            return NotImplemented
+        return NotImplemented
+    return wrap(func(*unwrapped_args, **unwrapped_kwargs))
+
+
+@implements([torch.ops.aten.cat.default, torch.ops.aten.stack.default])
+def cat_ops_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) <= 2, f"args: {unwrapped_args}"
+    # TODO: Use MapTensor type for filter
+    # First argument's dim
+    dim = unwrapped_args[0][0].dim()
+    size = unwrapped_args[0][0].size()
+    for a in unwrapped_args[0]:
+        if a.dim() > dim:
+            dim = a.dim()
+            size = a.size()
+    new_args = []
+    for a in unwrapped_args[0]:
+        if a.dim() == dim:
+            new_args.append(a)
+        else:
+            assert a.dim() + 1 == dim
+            new_args.append(a.unsqueeze(0).expand((size[0],) + a.size()))
+    orig_dim = unwrapped_args[1] if len(unwrapped_args) == 2 else 0
+    return wrap(func(new_args, wrap_dim(orig_dim, dim - 1) + 1))
+
+
+@implements(torch.ops.aten.select.int)
+def select_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 3, f"args: {unwrapped_args}"
+    return wrap(func(unwrapped_args[0], unwrapped_args[1] + 1, unwrapped_args[2]))
+
+
+@implements(torch.ops.aten.slice.Tensor)
+def slice_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 4, f"args: {unwrapped_args}"
+    dim = unwrapped_args[0].dim()
+    return wrap(
+        func(
+            unwrapped_args[0],
+            wrap_dim(unwrapped_args[1], dim - 1) + 1,
+            unwrapped_args[2],
+            unwrapped_args[3],
+        )
+    )
+
+
+@implements(
+    [
+        torch.ops.aten.mean.dim,
+        torch.ops.aten.max.dim,
+        torch.ops.aten.argmax.default,
+        torch.ops.aten.min.dim,
+        torch.ops.aten.any.dim,
+        torch.ops.aten.amax.default,
+        torch.ops.aten.amin.default,
+        torch.ops.aten.all.default,
+        torch.ops.aten.sum.dim_IntList,
+    ]
+)
+def reductions_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    # TODO: THIS MIGHT BE WRONG
+    if len(unwrapped_args) == 3 and len(unwrapped_kwargs) == 0:
+        assert len(unwrapped_args[1]) == 1
+        dim = unwrapped_args[0].dim()
+        return wrap(
+            func(
+                unwrapped_args[0],
+                [wrap_dim(u, dim - 1) + 1 for u in unwrapped_args[1]],
+                unwrapped_args[2],
+            )
+        )
+    if len(unwrapped_args) == 2 and len(unwrapped_kwargs) == 1:
+        assert len(unwrapped_args[1]) == 1
+        dim = unwrapped_args[0].dim()
+        return wrap(
+            func(
+                unwrapped_args[0],
+                [wrap_dim(u, dim - 1) + 1 for u in unwrapped_args[1]],
+                **unwrapped_kwargs,
+            )
+        )
+    if (
+        len(unwrapped_args) == 2
+        and len(unwrapped_kwargs) == 0
+        and type(unwrapped_args[1]) == list
+    ):
+        assert len(unwrapped_args[1]) == 1
+        dim = unwrapped_args[0].dim()
+        return wrap(
+            func(
+                unwrapped_args[0], [wrap_dim(u, dim - 1) + 1 for u in unwrapped_args[1]]
+            )
+        )
+    if (
+        len(unwrapped_args) == 2
+        and len(unwrapped_kwargs) == 0
+        and type(unwrapped_args[1]) == int
+    ):
+        dim = unwrapped_args[0].dim()
+        return wrap(func(unwrapped_args[0], wrap_dim(unwrapped_args[1], dim - 1) + 1))
+    if len(args) == 1 and len(kwargs) == 0:
+        return wrap(func(unwrapped_args[0]))
+    return NotImplemented
+
+
+@implements([torch.ops.aten._unsafe_view.default, torch.ops.aten.expand.default])
+def view_ops_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    input_size = unwrapped_args[0].size()
+    bigger_size = list(input_size[:1]) + unwrapped_args[1]
+    return wrap(func(unwrapped_args[0], bigger_size))
+
+
+@implements(torch.ops.aten.view.default)
+def view_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    input_size = unwrapped_args[0].size()
+    bigger_size = list(input_size[:1]) + unwrapped_args[1]
+    if unwrapped_args[0].size() == tuple(bigger_size):
+        return wrap(args[0].elems)
+    return wrap(unwrapped_args[0].reshape(bigger_size))
+
+
+@implements([torch.ops.aten.mm.default, torch.ops.aten.bmm.default])
+def mm_ops_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    return wrap(torch.matmul(*unwrapped_args))
+
+
+@implements(torch.ops.aten.unsqueeze.default)
+def unsqueeze_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    new_i = unwrapped_args[1]
+    if new_i >= 0:
+        new_i += 1
+    return wrap(func(unwrapped_args[0], new_i))
+
+
+@implements(torch.ops.aten.squeeze.dim)
+def squeeze_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    new_i = unwrapped_args[1]
+    if new_i >= 0:
+        new_i += 1
+    return wrap(func(unwrapped_args[0], new_i))
+
+
+@implements(torch.ops.aten.addmm.default)
+def addmm_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 3, f"args: {unwrapped_args}"
+    return wrap(torch.matmul(unwrapped_args[1], unwrapped_args[2]) + unwrapped_args[0])
+
+
+@implements(torch.ops.aten.convolution.default)
+def convolution_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 9, f"args: {unwrapped_args}"
+    a = unwrapped_args[0]
+    a = unwrapped_args[0].flatten(0, 1)
+    # TODO: It's scary that this .contiguous seems necessary, but I we're below composite conv
+    # which might expected contiguous output
+    resa = func(*((a,) + unwrapped_args[1:])).contiguous()
+    resb = resa.view(
+        (unwrapped_args[0].size(0), unwrapped_args[0].size(1)) + resa.size()[1:]
+    )
+    return wrap(resb)
+
+
+@implements(torch.ops.aten.upsample_bilinear2d.default)
+def upsample_bilinear2d_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 3, f"args: {unwrapped_args}"
+    a = unwrapped_args[0]
+    a = unwrapped_args[0].flatten(0, 1)
+    # NOTE: It's scary that this .contiguous seems necessary, but we're below composite upsample
+    # which might expected contiguous output
+    resa = func(*((a,) + unwrapped_args[1:])).contiguous()
+    resb = resa.view(
+        (unwrapped_args[0].size(0), unwrapped_args[0].size(1)) + resa.size()[1:]
+    )
+    return wrap(resb)
+
+
+@implements(torch.ops.aten.transpose.int)
+def transpose_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 3, f"args: {unwrapped_args}"
+    dim = unwrapped_args[0].dim()
+    return wrap(
+        func(
+            unwrapped_args[0],
+            wrap_dim(unwrapped_args[1], dim - 1) + 1,
+            wrap_dim(unwrapped_args[2], dim - 1) + 1,
+        )
+    )
+
+
+@implements(torch.ops.aten.unbind.int)
+def unbind_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    dim = unwrapped_args[0].dim()
+    return wrap(func(unwrapped_args[0], wrap_dim(unwrapped_args[1], dim - 1) + 1))
+
+
+@implements(torch.ops.aten.permute.default)
+def permute_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    dim = unwrapped_args[0].dim()
+    return wrap(
+        func(
+            unwrapped_args[0],
+            ([0] + [wrap_dim(u, dim - 1) + 1 for u in unwrapped_args[1]]),
+        )
+    )
+
+
+@implements(torch.ops.aten._scaled_dot_product_efficient_attention.default)
+def _scaled_dot_product_efficient_attention_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(args) == 5
+    if all(isinstance(a, MapTensor) for a in args[:3]):
+        # assert len(unwrapped_kwargs) == 0
+        assert len(unwrapped_args) == 5, f"args: {unwrapped_args}"
+        assert unwrapped_args[0].dim() == 5
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 5
+        sdpa_res = wrap(
+            func(
+                unwrapped_args[0].flatten(0, 1),
+                unwrapped_args[1].flatten(0, 1),
+                unwrapped_args[2].flatten(0, 1),
+                unwrapped_args[3],
+                unwrapped_args[4],
+                **unwrapped_kwargs,
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if isinstance(args[0], MapTensor) and not any(
+        isinstance(a, MapTensor) for a in args[1:]
+    ):
+        # assert len(unwrapped_kwargs) == 0
+        assert len(unwrapped_args) == 5, f"args: {unwrapped_args}"
+        assert unwrapped_args[0].dim() == 5
+        assert unwrapped_args[1].dim() == 4
+        assert unwrapped_args[2].dim() == 4
+        a0 = unwrapped_args[0]
+        a1_size = unwrapped_args[1].size()
+        a1 = unwrapped_args[1].unsqueeze(0).expand((a0.size(0),) + a1_size)
+        a2 = unwrapped_args[2].unsqueeze(0).expand((a0.size(0),) + a1_size)
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1),
+                a1.flatten(0, 1),
+                a2.flatten(0, 1),
+                unwrapped_args[3],
+                unwrapped_args[4],
+                **unwrapped_kwargs,
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if (
+        (not isinstance(args[0], MapTensor))
+        and isinstance(args[1], MapTensor)
+        and (not isinstance(args[2], MapTensor))
+    ):
+        assert len(unwrapped_kwargs) == 0
+        assert len(unwrapped_args) == 5, f"args: {unwrapped_args}"
+        assert unwrapped_args[0].dim() == 4
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 4
+        a1_size = unwrapped_args[1].size()
+        a0 = (
+            unwrapped_args[0]
+            .unsqueeze(0)
+            .expand((a1_size[0],) + unwrapped_args[0].size()[1:])
+        )
+        a2 = (
+            unwrapped_args[2]
+            .unsqueeze(0)
+            .expand((a1_size[0],) + unwrapped_args[2].size()[1:])
+        )
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1),
+                a1.flatten(0, 1),
+                a2.flatten(0, 1),
+                unwrapped_args[3],
+                unwrapped_args[4],
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if (
+        (not isinstance(args[0], MapTensor))
+        and isinstance(args[1], MapTensor)
+        and isinstance(args[2], MapTensor)
+    ):
+        # assert len(unwrapped_kwargs) == 0
+        assert len(unwrapped_args) == 5, f"args: {unwrapped_args}"
+        assert unwrapped_args[0].dim() == 4
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 5
+        a0_size = unwrapped_args[0].size()
+        a1_size = unwrapped_args[1].size()
+        a0 = unwrapped_args[0].unsqueeze(0).expand((a1_size[0],) + a0_size)
+        a1 = unwrapped_args[1]
+        a2 = unwrapped_args[2]
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1),
+                a1.flatten(0, 1),
+                a2.flatten(0, 1),
+                unwrapped_args[3],
+                unwrapped_args[4],
+                **unwrapped_kwargs,
+            )
+        )
+        return (wrap(sdpa_res[0].view((a1_size[0],) + a0_size)),) + sdpa_res[1:]
+    return NotImplemented
+
+
+@implements(torch.ops.aten._scaled_dot_product_flash_attention.default)
+def _scaled_dot_product_flash_attention_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(args) == 3
+    assert len(unwrapped_kwargs) == 1
+    assert len(unwrapped_args) == 3, f"args: {unwrapped_args}"
+    if all(isinstance(a, MapTensor) for a in args[:3]):
+        assert unwrapped_args[0].dim() == 5
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 5
+        sdpa_res = wrap(
+            func(
+                unwrapped_args[0].flatten(0, 1),
+                unwrapped_args[1].flatten(0, 1),
+                unwrapped_args[2].flatten(0, 1),
+                **unwrapped_kwargs,
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if isinstance(args[0], MapTensor) and not any(
+        isinstance(a, MapTensor) for a in args[1:]
+    ):
+        assert unwrapped_args[0].dim() == 5
+        assert unwrapped_args[1].dim() == 4
+        assert unwrapped_args[2].dim() == 4
+        a0 = unwrapped_args[0]
+        a1_size = unwrapped_args[1].size()
+        a1 = unwrapped_args[1].unsqueeze(0).expand((a0.size(0),) + a1_size)
+        a2 = unwrapped_args[2].unsqueeze(0).expand((a0.size(0),) + a1_size)
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1), a1.flatten(0, 1), a2.flatten(0, 1), **unwrapped_kwargs
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if (
+        (not isinstance(args[0], MapTensor))
+        and isinstance(args[1], MapTensor)
+        and (not isinstance(args[2], MapTensor))
+    ):
+        assert unwrapped_args[0].dim() == 4
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 4
+        a1_size = unwrapped_args[1].size()
+        a0 = (
+            unwrapped_args[0]
+            .unsqueeze(0)
+            .expand((a1_size[0],) + unwrapped_args[0].size()[1:])
+        )
+        a2 = (
+            unwrapped_args[2]
+            .unsqueeze(0)
+            .expand((a1_size[0],) + unwrapped_args[2].size()[1:])
+        )
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1), a1.flatten(0, 1), a2.flatten(0, 1), **unwrapped_kwargs
+            )
+        )
+        return (wrap(sdpa_res[0].view(unwrapped_args[0].size())),) + sdpa_res[1:]
+    if (
+        (not isinstance(args[0], MapTensor))
+        and isinstance(args[1], MapTensor)
+        and isinstance(args[2], MapTensor)
+    ):
+        assert unwrapped_args[0].dim() == 4
+        assert unwrapped_args[1].dim() == 5
+        assert unwrapped_args[2].dim() == 5
+        a0_size = unwrapped_args[0].size()
+        a1_size = unwrapped_args[1].size()
+        a0 = unwrapped_args[0].unsqueeze(0).expand((a1_size[0],) + a0_size)
+        a1 = unwrapped_args[1]
+        a2 = unwrapped_args[2]
+        sdpa_res = wrap(
+            func(
+                a0.flatten(0, 1), a1.flatten(0, 1), a2.flatten(0, 1), **unwrapped_kwargs
+            )
+        )
+        return (wrap(sdpa_res[0].view((a1_size[0],) + a0_size)),) + sdpa_res[1:]
+    return NotImplemented
+
+
+# torch.ops.aten._unsafe_index.Tensor is only needed by inductor for compile
+@implements([torch.ops.aten._unsafe_index.Tensor, torch.ops.aten.index.Tensor])
+def index_ops_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    assert len(unwrapped_kwargs) == 0
+    assert len(unwrapped_args) == 2, f"args: {unwrapped_args}"
+    # if len(args[1]) == 1 and isinstance(args[1][0], MapTensor) and isinstance(args[0], MapTensor):
+    #     return wrap(func(*unwrapped_args))
+    if (
+        len(args[1]) == 1
+        and isinstance(args[1][0], MapTensor)
+        and not isinstance(args[0], MapTensor)
+    ):
+        tensors = [
+            func(args[0], [args[1][0].elems[i]]) for i in range(len(args[1][0].elems))
+        ]
+        values = torch.cat(tensors)
+        lengths = torch.tensor([0] + [t.size(0) for t in tensors], pin_memory=True).to(
+            values.device, non_blocking=True
+        )
+        offsets = torch.cumsum(lengths, dim=0)
+        nt = nested_view_from_values_offsets(values, offsets)
+        assert nt.layout == torch.jagged
+        return wrap(nt)
+    if (
+        isinstance(args[0], MapTensor)
+        and not isinstance(args[1][0], MapTensor)
+        and len(args[1]) == 1
+    ):
+        return wrap(func(args[0].elems, [args[1][0].unsqueeze(0)]))
+    if (
+        isinstance(args[0], MapTensor)
+        and not isinstance(args[1][0], MapTensor)
+        and isinstance(args[1][1], MapTensor)
+        and len(args[1]) == 2
+    ):
+        res = []
+        for a0, a11 in zip(args[0].elems.unbind(), args[1][1].elems.unbind()):
+            res.append(func(a0, [args[1][0], a11]))
+        return wrap(torch.stack(res))
+    if (
+        isinstance(args[0], MapTensor)
+        and isinstance(args[1][0], MapTensor)
+        and len(args[1]) == 1
+    ):
+        tensors = [
+            func(args[0].elems[i], [args[1][0].elems[i]])
+            for i in range(len(args[0].elems))
+        ]
+        values = torch.cat(tensors)
+        lengths = torch.tensor([0] + [t.size(0) for t in tensors], pin_memory=True).to(
+            values.device, non_blocking=True
+        )
+        offsets = torch.cumsum(lengths, dim=0)
+        nt = nested_view_from_values_offsets(values, offsets)
+        assert nt.layout == torch.jagged
+        return wrap(nt)
+    a = unwrapped_args[0]
+    a = unwrapped_args[0].flatten(0, 1)
+    resa = func(a, args[1])
+    resb = resa.view(
+        (unwrapped_args[0].size(0), unwrapped_args[0].size(1)) + resa.size()[1:]
+    )
+    return wrap(resb)
+
+
+# Prims
+@implements(torch.ops.aten.dim.default)
+def dim_impl(func, types, args, kwargs):
+    assert len(args) == 1
+    assert len(kwargs) == 0
+    ret_dim = func(args[0].elems) - 1
+    assert ret_dim >= 0
+    return ret_dim
+
+
+@implements(torch.ops.aten.sym_size.default)
+def sym_impl(func, types, args, kwargs):
+    assert len(args) == 1
+    assert len(kwargs) == 0
+    elems_size = func(args[0].elems)
+    assert len(elems_size) > 0
+    return elems_size[1:]
+
+
+@implements(torch.ops.aten.is_contiguous.default)
+def is_contiguous_impl(func, types, args, kwargs):
+    assert len(args) == 1
+    assert len(kwargs) == 0
+    return func(args[0].elems)
+
+
+@implements(
+    [
+        torch.ops.aten.clamp.default,
+        torch.ops.aten.clone.default,
+        torch.ops.aten.cos.default,
+        torch.ops.aten.div.Tensor,
+        torch.ops.aten.eq.Scalar,
+        torch.ops.aten.gelu.default,
+        torch.ops.aten.mul.Tensor,
+        torch.ops.aten.pow.Tensor_Scalar,
+        torch.ops.aten.relu.default,
+        torch.ops.aten.sigmoid.default,
+        torch.ops.aten.sin.default,
+        torch.ops.aten.sqrt.default,
+        torch.ops.aten.sub.Tensor,
+        torch.ops.aten.unbind.int,
+        torch.ops.aten.where.self,
+        torch.ops.aten.zeros_like.default,
+        torch.ops.aten._to_copy.default,
+        torch.ops.aten.gt.Scalar,
+        torch.ops.aten.ge.Scalar,
+        torch.ops.aten.bitwise_not.default,
+        torch.ops.aten.lt.Tensor,
+        torch.ops.aten.bitwise_or.Tensor,
+        torch.ops.aten.eq.Tensor,
+        torch.ops.aten.abs.default,
+        torch.ops.aten.ne.Scalar,
+        torch.ops.aten.le.Tensor,
+        torch.ops.aten.view_as_complex.default,
+        torch.ops.aten.view_as_real.default,
+        torch.ops.aten.neg.default,
+        torch.ops.aten.le.Scalar,
+        torch.ops.aten.rsub.Scalar,
+        # Sketchy new in place ops
+        torch.ops.aten.bitwise_and_.Tensor,
+        torch.ops.aten.bitwise_or_.Tensor,
+        torch.ops.aten.le.Tensor,
+        torch.ops.aten.logical_and.default,
+        # in place ops
+        torch.ops.aten.add_.Tensor,
+        torch.ops.aten.copy_.default,
+        # Prims
+        torch.ops.prim.layout.default,
+    ]
+)
+def forwardables_impl(func, types, args, kwargs):
+    unwrapped_args = tree_map(unwrap, args)
+    unwrapped_kwargs = tree_map(unwrap, kwargs)
+    return wrap(func(*unwrapped_args, **unwrapped_kwargs))
+
+
+def run_invariant_test(res, func, args, kwargs):
+    # Compares 0th element of list of results with
+    # func applied to 0th arg and kwarg.
+    # Rough test to maintain per-op accuracy.
+    if isinstance(res, torch.Tensor):
+        unwrapped_args_0 = tree_map(lambda x: unwrap_i(x, 0), args)
+        unwrapped_kwargs_0 = tree_map(lambda x: unwrap_i(x, 0), kwargs)
+        if func == torch.ops.aten.view.default:
+            res_0 = torch.ops.aten.reshape.default(
+                *unwrapped_args_0, **unwrapped_kwargs_0
+            )
+        else:
+            res_0 = func(*unwrapped_args_0, **unwrapped_kwargs_0)
+        # TODO: Extend this all elems not just elems[0]
+        if res.elems[0].size() != res_0.size():
+            import pdb
+
+            pdb.set_trace()
+        if not torch.allclose(res.elems[0], res_0, atol=1e-3, rtol=1e-3):
+            import pdb
+
+            pdb.set_trace()
+    else:
+        pass
+        # print("res got type: ", type(res))
+        # import pdb; pdb.set_trace()
+    return res
+
+
+class MapTensor(torch.Tensor):
+    @staticmethod
+    def __new__(cls, elems):
+        # print("elems.layout: ", elems.layout)
+        return torch.Tensor._make_wrapper_subclass(
+            cls,
+            elems.shape[1:],
+            dtype=elems.dtype,
+            device=elems.device,
+            layout=elems.layout,
+            dispatch_layout=True,
+            dispatch_sizes_strides_policy=(
+                "sizes" if elems.layout == torch.jagged else None
+            ),
+            storage_size=(
+                elems._values.untyped_storage().size()
+                if elems.layout == torch.jagged
+                else None
+            ),
+        )
+
+    def __init__(self, elems):
+        self.elems = elems
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs=None):
+        if func in MAP_TENSOR_ATEN_OP_TABLE:
+            res = MAP_TENSOR_ATEN_OP_TABLE[func](func, types, args, kwargs)
+            # run_invariant_test(res, func, args, kwargs)
+            return res
+        return NotImplemented
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        with torch._C.DisableTorchFunctionSubclass():
+            return func(*args, **kwargs)
+
+    # flatten/unflatten is needed for compile
+    def __tensor_flatten__(self):
+        ctx = {}
+        inner_tensors = ["elems"]
+        return inner_tensors, ctx
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors: Dict, meta, outer_size, outer_stride):
+        # inner tensors: _values, _offsets, [_lengths], [_min_seqlen], [_max_seqlen]
+        assert len(inner_tensors) == 1, f"{inner_tensors}"
+        elems = inner_tensors["elems"]
+
+        return MapTensor(elems)
+
+    def __repr__(self):
+        return f"MapTensor({self.elems.size()})"
+
+    def pin_memory(self):
+        elems = self.elems.pin_memory()
+        return wrap(elems)
+
+
+# ts is a higher dim Tensor
+def to_map_tensor(ts: torch.Tensor):
+    return MapTensor(ts)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/backbones/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/backbones/hieradet.py

@@ -0,0 +1,332 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from functools import partial
+from typing import List, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from iopath.common.file_io import g_pathmgr
+
+from torchao._models.sam2.modeling.backbones.utils import (
+    PatchEmbed,
+    window_partition,
+    window_unpartition,
+)
+from torchao._models.sam2.modeling.sam2_utils import MLP, DropPath
+
+
+def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor:
+    if pool is None:
+        return x
+    # (B, H, W, C) -> (B, C, H, W)
+    x = x.permute(0, 3, 1, 2)
+    x = pool(x)
+    # (B, C, H', W') -> (B, H', W', C)
+    x = x.permute(0, 2, 3, 1)
+    if norm:
+        x = norm(x)
+
+    return x
+
+
+class MultiScaleAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_out: int,
+        num_heads: int,
+        q_pool: nn.Module = None,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.dim_out = dim_out
+        self.num_heads = num_heads
+        self.q_pool = q_pool
+        self.qkv = nn.Linear(dim, dim_out * 3)
+        self.proj = nn.Linear(dim_out, dim_out)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, H, W, _ = x.shape
+        # qkv with shape (B, H * W, 3, nHead, C)
+        qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)
+        # q, k, v with shape (B, H * W, nheads, C)
+        q, k, v = torch.unbind(qkv, 2)
+
+        # Q pooling (for downsample at stage changes)
+        if self.q_pool:
+            q = do_pool(q.reshape(B, H, W, -1), self.q_pool)
+            H, W = q.shape[1:3]  # downsampled shape
+            q = q.reshape(B, H * W, self.num_heads, -1)
+
+        # TODO: This fails on batch sizes bigger than 2^16 - 1
+        # Torch's SDPA expects [B, nheads, H*W, C] so we transpose
+        if q.size(0) > 65535:
+            num_chunks = (q.size(0) + 65535) // 65535
+            q_chunks = q.chunk(num_chunks)
+            k_chunks = k.chunk(num_chunks)
+            v_chunks = v.chunk(num_chunks)
+            x_chunks = []
+            for qi, ki, vi in zip(q_chunks, k_chunks, v_chunks):
+                xi = F.scaled_dot_product_attention(
+                    qi.transpose(1, 2),
+                    ki.transpose(1, 2),
+                    vi.transpose(1, 2),
+                )
+                x_chunks.append(xi)
+            x = torch.cat(x_chunks)
+        else:
+            x = F.scaled_dot_product_attention(
+                q.transpose(1, 2),
+                k.transpose(1, 2),
+                v.transpose(1, 2),
+            )
+        # Transpose back
+        x = x.transpose(1, 2)
+        x = x.reshape(B, H, W, -1)
+
+        x = self.proj(x)
+
+        return x
+
+
+class MultiScaleBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_out: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        drop_path: float = 0.0,
+        norm_layer: Union[nn.Module, str] = "LayerNorm",
+        q_stride: Tuple[int, int] = None,
+        act_layer: nn.Module = nn.GELU,
+        window_size: int = 0,
+    ):
+        super().__init__()
+
+        if isinstance(norm_layer, str):
+            norm_layer = partial(getattr(nn, norm_layer), eps=1e-6)
+
+        self.dim = dim
+        self.dim_out = dim_out
+        self.norm1 = norm_layer(dim)
+
+        self.window_size = window_size
+
+        self.pool, self.q_stride = None, q_stride
+        if self.q_stride:
+            self.pool = nn.MaxPool2d(
+                kernel_size=q_stride, stride=q_stride, ceil_mode=False
+            )
+
+        self.attn = MultiScaleAttention(
+            dim,
+            dim_out,
+            num_heads=num_heads,
+            q_pool=self.pool,
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim_out)
+        self.mlp = MLP(
+            dim_out,
+            int(dim_out * mlp_ratio),
+            dim_out,
+            num_layers=2,
+            activation=act_layer,
+        )
+
+        if dim != dim_out:
+            self.proj = nn.Linear(dim, dim_out)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        shortcut = x  # B, H, W, C
+        x = self.norm1(x)
+
+        # Skip connection
+        if self.dim != self.dim_out:
+            shortcut = do_pool(self.proj(x), self.pool)
+
+        # Window partition
+        window_size = self.window_size
+        if window_size > 0:
+            H, W = x.shape[1], x.shape[2]
+            x, pad_hw = window_partition(x, window_size)
+
+        # Window Attention + Q Pooling (if stage change)
+        x = self.attn(x)
+        if self.q_stride:
+            # Shapes have changed due to Q pooling
+            window_size = self.window_size // self.q_stride[0]
+            H, W = shortcut.shape[1:3]
+
+            pad_h = (window_size - H % window_size) % window_size
+            pad_w = (window_size - W % window_size) % window_size
+            pad_hw = (H + pad_h, W + pad_w)
+
+        # Reverse window partition
+        if self.window_size > 0:
+            x = window_unpartition(x, window_size, pad_hw, (H, W))
+
+        x = shortcut + self.drop_path(x)
+        # MLP
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class Hiera(nn.Module):
+    """
+    Reference: https://arxiv.org/abs/2306.00989
+    """
+
+    def __init__(
+        self,
+        embed_dim: int = 96,  # initial embed dim
+        num_heads: int = 1,  # initial number of heads
+        drop_path_rate: float = 0.0,  # stochastic depth
+        q_pool: int = 3,  # number of q_pool stages
+        q_stride: Tuple[int, int] = (2, 2),  # downsample stride bet. stages
+        stages: Tuple[int, ...] = (2, 3, 16, 3),  # blocks per stage
+        dim_mul: float = 2.0,  # dim_mul factor at stage shift
+        head_mul: float = 2.0,  # head_mul factor at stage shift
+        window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14),
+        # window size per stage, when not using global att.
+        window_spec: Tuple[int, ...] = (
+            8,
+            4,
+            14,
+            7,
+        ),
+        # global attn in these blocks
+        global_att_blocks: Tuple[int, ...] = (
+            12,
+            16,
+            20,
+        ),
+        weights_path=None,
+        return_interm_layers=True,  # return feats from every stage
+    ):
+        super().__init__()
+
+        assert len(stages) == len(window_spec)
+        self.window_spec = window_spec
+
+        depth = sum(stages)
+        self.q_stride = q_stride
+        self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]
+        assert 0 <= q_pool <= len(self.stage_ends[:-1])
+        self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]
+        self.return_interm_layers = return_interm_layers
+
+        self.patch_embed = PatchEmbed(
+            embed_dim=embed_dim,
+        )
+        # Which blocks have global att?
+        self.global_att_blocks = global_att_blocks
+
+        # Windowed positional embedding (https://arxiv.org/abs/2311.05613)
+        self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size
+        self.pos_embed = nn.Parameter(
+            torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size)
+        )
+        self.pos_embed_window = nn.Parameter(
+            torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0])
+        )
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+
+        cur_stage = 1
+        self.blocks = nn.ModuleList()
+
+        for i in range(depth):
+            dim_out = embed_dim
+            # lags by a block, so first block of
+            # next stage uses an initial window size
+            # of previous stage and final window size of current stage
+            window_size = self.window_spec[cur_stage - 1]
+
+            if self.global_att_blocks is not None:
+                window_size = 0 if i in self.global_att_blocks else window_size
+
+            if i - 1 in self.stage_ends:
+                dim_out = int(embed_dim * dim_mul)
+                num_heads = int(num_heads * head_mul)
+                cur_stage += 1
+
+            block = MultiScaleBlock(
+                dim=embed_dim,
+                dim_out=dim_out,
+                num_heads=num_heads,
+                drop_path=dpr[i],
+                q_stride=self.q_stride if i in self.q_pool_blocks else None,
+                window_size=window_size,
+            )
+
+            embed_dim = dim_out
+            self.blocks.append(block)
+
+        self.channel_list = (
+            [self.blocks[i].dim_out for i in self.stage_ends[::-1]]
+            if return_interm_layers
+            else [self.blocks[-1].dim_out]
+        )
+
+        if weights_path is not None:
+            with g_pathmgr.open(weights_path, "rb") as f:
+                chkpt = torch.load(f, map_location="cpu")
+            logging.info("loading Hiera", self.load_state_dict(chkpt, strict=False))
+
+    def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:
+        h, w = hw
+        window_embed = self.pos_embed_window
+        pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic")
+        pos_embed = pos_embed + window_embed.tile(
+            [x // y for x, y in zip(pos_embed.shape, window_embed.shape)]
+        )
+        pos_embed = pos_embed.permute(0, 2, 3, 1)
+        return pos_embed
+
+    def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
+        x = self.patch_embed(x)
+        # x: (B, H, W, C)
+
+        # Add pos embed
+        x = x + self._get_pos_embed(x.shape[1:3])
+
+        outputs = []
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if (i == self.stage_ends[-1]) or (
+                i in self.stage_ends and self.return_interm_layers
+            ):
+                feats = x.permute(0, 3, 1, 2)
+                outputs.append(feats)
+
+        return outputs
+
+    def get_layer_id(self, layer_name):
+        # https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
+        num_layers = self.get_num_layers()
+
+        if layer_name.find("rel_pos") != -1:
+            return num_layers + 1
+        elif layer_name.find("pos_embed") != -1:
+            return 0
+        elif layer_name.find("patch_embed") != -1:
+            return 0
+        elif layer_name.find("blocks") != -1:
+            return int(layer_name.split("blocks")[1].split(".")[1]) + 1
+        else:
+            return num_layers + 1
+
+    def get_num_layers(self) -> int:
+        return len(self.blocks)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/backbones/image_encoder.py

@@ -0,0 +1,141 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class ImageEncoder(nn.Module):
+    def __init__(
+        self,
+        trunk: nn.Module,
+        neck: nn.Module,
+        scalp: int = 0,
+    ):
+        super().__init__()
+        self.trunk = trunk
+        self.neck = neck
+        self.scalp = scalp
+        assert self.trunk.channel_list == self.neck.backbone_channel_list, (
+            f"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}"
+        )
+
+    def forward(self, sample: torch.Tensor):
+        # Forward through backbone
+        with torch.autograd.profiler.record_function("self.neck(self.trunk(sample))"):
+            from torchao._models.sam2.map_tensor import MapTensor, to_map_tensor
+
+            if isinstance(sample, MapTensor):
+                features, pos = self.neck(self.trunk(sample.elems.flatten(0, 1)))
+                features = [to_map_tensor(t.unsqueeze(1)) for t in features]
+                pos = [to_map_tensor(t.unsqueeze(1)) for t in pos]
+            else:
+                features, pos = self.neck(self.trunk(sample))
+        if self.scalp > 0:
+            # Discard the lowest resolution features
+            features, pos = features[: -self.scalp], pos[: -self.scalp]
+
+        src = features[-1]
+        output = {
+            "vision_features": src,
+            "vision_pos_enc": pos,
+            "backbone_fpn": features,
+        }
+        return output
+
+
+class FpnNeck(nn.Module):
+    """
+    A modified variant of Feature Pyramid Network (FPN) neck
+    (we remove output conv and also do bicubic interpolation similar to ViT
+    pos embed interpolation)
+    """
+
+    def __init__(
+        self,
+        position_encoding: nn.Module,
+        d_model: int,
+        backbone_channel_list: List[int],
+        kernel_size: int = 1,
+        stride: int = 1,
+        padding: int = 0,
+        fpn_interp_model: str = "bilinear",
+        fuse_type: str = "sum",
+        fpn_top_down_levels: Optional[List[int]] = None,
+    ):
+        """Initialize the neck
+        :param trunk: the backbone
+        :param position_encoding: the positional encoding to use
+        :param d_model: the dimension of the model
+        :param neck_norm: the normalization to use
+        """
+        super().__init__()
+        self.position_encoding = position_encoding
+        self.convs = nn.ModuleList()
+        self.backbone_channel_list = backbone_channel_list
+        self.d_model = d_model
+        for dim in backbone_channel_list:
+            current = nn.Sequential()
+            current.add_module(
+                "conv",
+                nn.Conv2d(
+                    in_channels=dim,
+                    out_channels=d_model,
+                    kernel_size=kernel_size,
+                    stride=stride,
+                    padding=padding,
+                ),
+            )
+
+            self.convs.append(current)
+        self.fpn_interp_model = fpn_interp_model
+        assert fuse_type in ["sum", "avg"]
+        self.fuse_type = fuse_type
+
+        # levels to have top-down features in its outputs
+        # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3
+        # have top-down propagation, while outputs of level 0 and level 1 have only
+        # lateral features from the same backbone level.
+        if fpn_top_down_levels is None:
+            # default is to have top-down features on all levels
+            fpn_top_down_levels = range(len(self.convs))
+        self.fpn_top_down_levels = list(fpn_top_down_levels)
+
+    def forward(self, xs: List[torch.Tensor]):
+        out = [None] * len(self.convs)
+        pos = [None] * len(self.convs)
+        assert len(xs) == len(self.convs)
+        # fpn forward pass
+        # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py
+        prev_features = None
+        # forward in top-down order (from low to high resolution)
+        n = len(self.convs) - 1
+        for i in range(n, -1, -1):
+            x = xs[i]
+            lateral_features = self.convs[n - i](x)
+            if i in self.fpn_top_down_levels and prev_features is not None:
+                top_down_features = F.interpolate(
+                    prev_features.to(dtype=torch.float32),
+                    scale_factor=2.0,
+                    mode=self.fpn_interp_model,
+                    align_corners=(
+                        None if self.fpn_interp_model == "nearest" else False
+                    ),
+                    antialias=False,
+                )
+                prev_features = lateral_features + top_down_features
+                if self.fuse_type == "avg":
+                    prev_features /= 2
+            else:
+                prev_features = lateral_features
+            x_out = prev_features
+            out[i] = x_out
+            pos[i] = self.position_encoding(x_out).to(x_out.dtype)
+
+        return out, pos

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/backbones/utils.py

@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Some utilities for backbones, in particular for windowing"""
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def window_partition(x, window_size):
+    """
+    Partition into non-overlapping windows with padding if needed.
+    Args:
+        x (tensor): input tokens with [B, H, W, C].
+        window_size (int): window size.
+    Returns:
+        windows: windows after partition with [B * num_windows, window_size, window_size, C].
+        (Hp, Wp): padded height and width before partition
+    """
+    B, H, W, C = x.shape
+
+    pad_h = (window_size - H % window_size) % window_size
+    pad_w = (window_size - W % window_size) % window_size
+    if pad_h > 0 or pad_w > 0:
+        x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
+    Hp, Wp = H + pad_h, W + pad_w
+
+    x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
+    windows = (
+        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    )
+    return windows, (Hp, Wp)
+
+
+def window_unpartition(windows, window_size, pad_hw, hw):
+    """
+    Window unpartition into original sequences and removing padding.
+    Args:
+        x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
+        window_size (int): window size.
+        pad_hw (Tuple): padded height and width (Hp, Wp).
+        hw (Tuple): original height and width (H, W) before padding.
+    Returns:
+        x: unpartitioned sequences with [B, H, W, C].
+    """
+    Hp, Wp = pad_hw
+    H, W = hw
+    B = windows.shape[0] // (Hp * Wp // window_size // window_size)
+    x = windows.view(
+        B, Hp // window_size, Wp // window_size, window_size, window_size, -1
+    )
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
+
+    if Hp > H or Wp > W:
+        x = x[:, :H, :W, :].contiguous()
+    return x
+
+
+class PatchEmbed(nn.Module):
+    """
+    Image to Patch Embedding.
+    """
+
+    def __init__(
+        self,
+        kernel_size: Tuple[int, ...] = (7, 7),
+        stride: Tuple[int, ...] = (4, 4),
+        padding: Tuple[int, ...] = (3, 3),
+        in_chans: int = 3,
+        embed_dim: int = 768,
+    ):
+        """
+        Args:
+            kernel_size (Tuple): kernel size of the projection layer.
+            stride (Tuple): stride of the projection layer.
+            padding (Tuple): padding size of the projection layer.
+            in_chans (int): Number of input image channels.
+            embed_dim (int):  embed_dim (int): Patch embedding dimension.
+        """
+        super().__init__()
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        # B C H W -> B H W C
+        x = x.permute(0, 2, 3, 1)
+        return x

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/memory_attention.py

@@ -0,0 +1,166 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional
+
+import torch
+from torch import Tensor, nn
+
+from torchao._models.sam2.modeling.sam.transformer import RoPEAttention
+from torchao._models.sam2.modeling.sam2_utils import get_activation_fn, get_clones
+
+
+class MemoryAttentionLayer(nn.Module):
+    def __init__(
+        self,
+        activation: str,
+        cross_attention: nn.Module,
+        d_model: int,
+        dim_feedforward: int,
+        dropout: float,
+        pos_enc_at_attn: bool,
+        pos_enc_at_cross_attn_keys: bool,
+        pos_enc_at_cross_attn_queries: bool,
+        self_attention: nn.Module,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.dim_feedforward = dim_feedforward
+        self.dropout_value = dropout
+        self.self_attn = self_attention
+        self.cross_attn_image = cross_attention
+
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation_str = activation
+        self.activation = get_activation_fn(activation)
+
+        # Where to add pos enc
+        self.pos_enc_at_attn = pos_enc_at_attn
+        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
+        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys
+
+    def _forward_sa(self, tgt, query_pos):
+        # Self-Attention
+        tgt2 = self.norm1(tgt)
+        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
+        tgt2 = self.self_attn(q, k, v=tgt2)
+        tgt = tgt + self.dropout1(tgt2)
+        return tgt
+
+    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
+        kwds = {}
+        if num_k_exclude_rope > 0:
+            assert isinstance(self.cross_attn_image, RoPEAttention)
+            kwds = {"num_k_exclude_rope": num_k_exclude_rope}
+
+        # Cross-Attention
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.cross_attn_image(
+            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
+            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
+            v=memory,
+            **kwds,
+        )
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        pos: Optional[Tensor] = None,
+        query_pos: Optional[Tensor] = None,
+        num_k_exclude_rope: int = 0,
+    ) -> torch.Tensor:
+        # Self-Attn, Cross-Attn
+        tgt = self._forward_sa(tgt, query_pos)
+        tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
+        # MLP
+        tgt2 = self.norm3(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout3(tgt2)
+        return tgt
+
+
+class MemoryAttention(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        pos_enc_at_input: bool,
+        layer: nn.Module,
+        num_layers: int,
+        batch_first: bool = True,  # Do layers expect batch first input?
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.layers = get_clones(layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = nn.LayerNorm(d_model)
+        self.pos_enc_at_input = pos_enc_at_input
+        self.batch_first = batch_first
+
+    def forward(
+        self,
+        curr: torch.Tensor,  # self-attention inputs
+        memory: torch.Tensor,  # cross-attention inputs
+        curr_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
+        memory_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
+        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
+    ):
+        if isinstance(curr, list):
+            assert isinstance(curr_pos, list)
+            assert len(curr) == len(curr_pos) == 1
+            curr, curr_pos = (
+                curr[0],
+                curr_pos[0],
+            )
+
+        assert curr.shape[1] == memory.shape[1], (
+            "Batch size must be the same for curr and memory"
+        )
+
+        output = curr
+        if self.pos_enc_at_input and curr_pos is not None:
+            output = output + 0.1 * curr_pos
+
+        if self.batch_first:
+            # Convert to batch first
+            output = output.transpose(0, 1)
+            curr_pos = curr_pos.transpose(0, 1)
+            memory = memory.transpose(0, 1)
+            memory_pos = memory_pos.transpose(0, 1)
+
+        for layer in self.layers:
+            kwds = {}
+            if isinstance(layer.cross_attn_image, RoPEAttention):
+                kwds = {"num_k_exclude_rope": num_obj_ptr_tokens}
+
+            output = layer(
+                tgt=output,
+                memory=memory,
+                pos=memory_pos,
+                query_pos=curr_pos,
+                **kwds,
+            )
+        normed_output = self.norm(output)
+
+        if self.batch_first:
+            # Convert back to seq first
+            normed_output = normed_output.transpose(0, 1)
+            curr_pos = curr_pos.transpose(0, 1)
+
+        return normed_output

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/memory_encoder.py

@@ -0,0 +1,181 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torchao._models.sam2.modeling.sam2_utils import DropPath, LayerNorm2d, get_clones
+
+
+class MaskDownSampler(nn.Module):
+    """
+    Progressively downsample a mask by total_stride, each time by stride.
+    Note that LayerNorm is applied per *token*, like in ViT.
+
+    With each downsample (by a factor stride**2), channel capacity increases by the same factor.
+    In the end, we linearly project to embed_dim channels.
+    """
+
+    def __init__(
+        self,
+        embed_dim=256,
+        kernel_size=4,
+        stride=4,
+        padding=0,
+        total_stride=16,
+        activation=nn.GELU,
+    ):
+        super().__init__()
+        num_layers = int(math.log2(total_stride) // math.log2(stride))
+        assert stride**num_layers == total_stride
+        self.encoder = nn.Sequential()
+        mask_in_chans, mask_out_chans = 1, 1
+        for _ in range(num_layers):
+            mask_out_chans = mask_in_chans * (stride**2)
+            self.encoder.append(
+                nn.Conv2d(
+                    mask_in_chans,
+                    mask_out_chans,
+                    kernel_size=kernel_size,
+                    stride=stride,
+                    padding=padding,
+                )
+            )
+            self.encoder.append(LayerNorm2d(mask_out_chans))
+            self.encoder.append(activation())
+            mask_in_chans = mask_out_chans
+
+        self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))
+
+    def forward(self, x):
+        return self.encoder(x)
+
+
+# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)
+class CXBlock(nn.Module):
+    r"""ConvNeXt Block. There are two equivalent implementations:
+    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+    We use (2) as we find it slightly faster in PyTorch
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+    """
+
+    def __init__(
+        self,
+        dim,
+        kernel_size=7,
+        padding=3,
+        drop_path=0.0,
+        layer_scale_init_value=1e-6,
+        use_dwconv=True,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv2d(
+            dim,
+            dim,
+            kernel_size=kernel_size,
+            padding=padding,
+            groups=dim if use_dwconv else 1,
+        )  # depthwise conv
+        self.norm = LayerNorm2d(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(
+            dim, 4 * dim
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = self.norm(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class Fuser(nn.Module):
+    def __init__(self, layer, num_layers, dim=None, input_projection=False):
+        super().__init__()
+        self.proj = nn.Identity()
+        self.layers = get_clones(layer, num_layers)
+
+        if input_projection:
+            assert dim is not None
+            self.proj = nn.Conv2d(dim, dim, kernel_size=1)
+
+    def forward(self, x):
+        # normally x: (N, C, H, W)
+        x = self.proj(x)
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+
+class MemoryEncoder(nn.Module):
+    def __init__(
+        self,
+        out_dim,
+        mask_downsampler,
+        fuser,
+        position_encoding,
+        in_dim=256,  # in_dim of pix_feats
+    ):
+        super().__init__()
+
+        self.mask_downsampler = mask_downsampler
+
+        self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)
+        self.fuser = fuser
+        self.position_encoding = position_encoding
+        self.out_proj = nn.Identity()
+        if out_dim != in_dim:
+            self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)
+
+    def forward(
+        self,
+        pix_feat: torch.Tensor,
+        masks: torch.Tensor,
+        skip_mask_sigmoid: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        ## Process masks
+        # sigmoid, so that less domain shift from gt masks which are bool
+        if not skip_mask_sigmoid:
+            masks = F.sigmoid(masks)
+        masks = self.mask_downsampler(masks)
+
+        ## Fuse pix_feats and downsampled masks
+        # in case the visual features are on CPU, cast them to CUDA
+        pix_feat = pix_feat.to(masks.device)
+
+        x = self.pix_feat_proj(pix_feat)
+        x = x + masks
+        x = self.fuser(x)
+        x = self.out_proj(x)
+
+        pos = self.position_encoding(x).to(x.dtype)
+
+        return {"vision_features": x, "vision_pos_enc": [pos]}

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/position_encoding.py

@@ -0,0 +1,226 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from typing import Any, Optional, Tuple
+
+import numpy as np
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention Is All You Need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self,
+        num_pos_feats,
+        temperature: int = 10000,
+        normalize: bool = True,
+        scale: Optional[float] = None,
+    ):
+        super().__init__()
+        assert num_pos_feats % 2 == 0, "Expecting even model width"
+        self.num_pos_feats = num_pos_feats // 2
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+        # self.cache = {}
+
+    def _encode_xy(self, x, y):
+        # The positions are expected to be normalized
+        assert len(x) == len(y) and x.ndim == y.ndim == 1
+        x_embed = x * self.scale
+        y_embed = y * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, None] / dim_t
+        pos_y = y_embed[:, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2
+        ).flatten(1)
+        pos_y = torch.stack(
+            (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2
+        ).flatten(1)
+        return pos_x, pos_y
+
+    @torch.no_grad()
+    def encode_boxes(self, x, y, w, h):
+        pos_x, pos_y = self._encode_xy(x, y)
+        pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
+        return pos
+
+    encode = encode_boxes  # Backwards compatibility
+
+    @torch.no_grad()
+    def encode_points(self, x, y, labels):
+        (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape
+        assert bx == by and nx == ny and bx == bl and nx == nl
+        pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())
+        pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)
+        pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
+        return pos
+
+    @torch.no_grad()
+    def forward(self, x: torch.Tensor):
+        # cache_key = (x.shape[-2], x.shape[-1])
+        # if cache_key in self.cache:
+        #     return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)
+        y_embed = (
+            torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)
+            .view(1, -1, 1)
+            .repeat(x.shape[0], 1, x.shape[-1])
+        )
+        x_embed = (
+            torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)
+            .view(1, 1, -1)
+            .repeat(x.shape[0], x.shape[-2], 1)
+        )
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        # self.cache[cache_key] = pos[0]
+        return pos
+
+
+class PositionEmbeddingRandom(nn.Module):
+    """
+    Positional encoding using random spatial frequencies.
+    """
+
+    def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
+        super().__init__()
+        if scale is None or scale <= 0.0:
+            scale = 1.0
+        self.register_buffer(
+            "positional_encoding_gaussian_matrix",
+            scale * torch.randn((2, num_pos_feats)),
+        )
+
+    def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
+        """Positionally encode points that are normalized to [0,1]."""
+        # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
+        coords = 2 * coords - 1
+        coords = coords @ self.positional_encoding_gaussian_matrix
+        coords = 2 * np.pi * coords
+        # outputs d_1 x ... x d_n x C shape
+        return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
+
+    def forward(self, size: Tuple[int, int]) -> torch.Tensor:
+        """Generate positional encoding for a grid of the specified size."""
+        h, w = size
+        device: Any = self.positional_encoding_gaussian_matrix.device
+        grid = torch.ones((h, w), device=device, dtype=torch.float32)
+        y_embed = grid.cumsum(dim=0) - 0.5
+        x_embed = grid.cumsum(dim=1) - 0.5
+        y_embed = y_embed / h
+        x_embed = x_embed / w
+
+        pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
+        return pe.permute(2, 0, 1)  # C x H x W
+
+    def forward_with_coords(
+        self, coords_input: torch.Tensor, image_size: Tuple[int, int]
+    ) -> torch.Tensor:
+        """Positionally encode points that are not normalized to [0,1]."""
+        coords = coords_input.clone()
+        coords[:, :, 0] = coords[:, :, 0] / image_size[1]
+        coords[:, :, 1] = coords[:, :, 1] / image_size[0]
+        return self._pe_encoding(coords.to(torch.float))  # B x N x C
+
+
+# Rotary Positional Encoding, adapted from:
+# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py
+# 2. https://github.com/naver-ai/rope-vit
+# 3. https://github.com/lucidrains/rotary-embedding-torch
+
+
+def init_t_xy(end_x: int, end_y: int, device=None):
+    t = torch.arange(end_x * end_y, dtype=torch.float32, device=device)
+    t_x = (t % end_x).float()
+    t_y = torch.div(t, end_x, rounding_mode="floor").float()
+    return t_x, t_y
+
+
+def compute_axial_cis(
+    dim: int, end_x: int, end_y: int, theta: float = 10000.0, device=None
+):
+    freqs_x = 1.0 / (
+        theta ** (torch.arange(0, dim, 4, device=device)[: (dim // 4)].float() / dim)
+    )
+    freqs_y = 1.0 / (
+        theta ** (torch.arange(0, dim, 4, device=device)[: (dim // 4)].float() / dim)
+    )
+
+    t_x, t_y = init_t_xy(end_x, end_y, device=device)
+    freqs_x = torch.outer(t_x, freqs_x)
+    freqs_y = torch.outer(t_y, freqs_y)
+    freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
+    freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
+    return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
+    shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
+    return freqs_cis.view(*shape)
+
+
+def apply_rotary_enc(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+    repeat_freqs_k: bool = False,
+):
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = (
+        torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+        if xk.shape[-2] != 0
+        else None
+    )
+    freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
+    if xk_ is None:
+        # no keys to rotate, due to dropout
+        return xq_out.type_as(xq).to(xq.device), xk
+    # repeat freqs along seq_len dim to match k seq_len
+    if repeat_freqs_k:
+        r = xk_.shape[-2] // xq_.shape[-2]
+        if freqs_cis.is_cuda:
+            freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
+        else:
+            # torch.repeat on complex numbers may not be supported on non-CUDA devices
+            # (freqs_cis has 4 dims and we repeat on dim 2) so we use expand + flatten
+            freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
+    return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam/mask_decoder.py

@@ -0,0 +1,317 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import List, Optional, Tuple, Type
+
+import torch
+from torch import nn
+
+from torchao._models.sam2.modeling.sam2_utils import MLP, LayerNorm2d
+
+
+class MaskDecoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        transformer_dim: int,
+        transformer: nn.Module,
+        num_multimask_outputs: int = 3,
+        activation: Type[nn.Module] = nn.GELU,
+        iou_head_depth: int = 3,
+        iou_head_hidden_dim: int = 256,
+        use_high_res_features: bool = False,
+        iou_prediction_use_sigmoid=False,
+        dynamic_multimask_via_stability=False,
+        dynamic_multimask_stability_delta=0.05,
+        dynamic_multimask_stability_thresh=0.98,
+        pred_obj_scores: bool = False,
+        pred_obj_scores_mlp: bool = False,
+        use_multimask_token_for_obj_ptr: bool = False,
+    ) -> None:
+        """
+        Predicts masks given an image and prompt embeddings, using a
+        transformer architecture.
+
+        Arguments:
+          transformer_dim (int): the channel dimension of the transformer
+          transformer (nn.Module): the transformer used to predict masks
+          num_multimask_outputs (int): the number of masks to predict
+            when disambiguating masks
+          activation (nn.Module): the type of activation to use when
+            upscaling masks
+          iou_head_depth (int): the depth of the MLP used to predict
+            mask quality
+          iou_head_hidden_dim (int): the hidden dimension of the MLP
+            used to predict mask quality
+        """
+        super().__init__()
+        self.transformer_dim = transformer_dim
+        self.transformer = transformer
+
+        self.num_multimask_outputs = num_multimask_outputs
+
+        self.iou_token = nn.Embedding(1, transformer_dim)
+        self.num_mask_tokens = num_multimask_outputs + 1
+        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
+
+        self.pred_obj_scores = pred_obj_scores
+        if self.pred_obj_scores:
+            self.obj_score_token = nn.Embedding(1, transformer_dim)
+        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
+
+        self.output_upscaling = nn.Sequential(
+            nn.ConvTranspose2d(
+                transformer_dim, transformer_dim // 4, kernel_size=2, stride=2
+            ),
+            LayerNorm2d(transformer_dim // 4),
+            activation(),
+            nn.ConvTranspose2d(
+                transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2
+            ),
+            activation(),
+        )
+        self.use_high_res_features = use_high_res_features
+        if use_high_res_features:
+            self.conv_s0 = nn.Conv2d(
+                transformer_dim, transformer_dim // 8, kernel_size=1, stride=1
+            )
+            self.conv_s1 = nn.Conv2d(
+                transformer_dim, transformer_dim // 4, kernel_size=1, stride=1
+            )
+
+        self.output_hypernetworks_mlps = nn.ModuleList(
+            [
+                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
+                for i in range(self.num_mask_tokens)
+            ]
+        )
+
+        self.iou_prediction_head = MLP(
+            transformer_dim,
+            iou_head_hidden_dim,
+            self.num_mask_tokens,
+            iou_head_depth,
+            sigmoid_output=iou_prediction_use_sigmoid,
+        )
+        if self.pred_obj_scores:
+            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
+            if pred_obj_scores_mlp:
+                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)
+
+        # When outputting a single mask, optionally we can dynamically fall back to the best
+        # multimask output token if the single mask output token gives low stability scores.
+        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
+        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
+        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh
+        self._src_dtype = torch.float32
+
+    def forward(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        multimask_output: bool,
+        repeat_image: bool,
+        high_res_features: Optional[List[torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Predict masks given image and prompt embeddings.
+
+        Arguments:
+          image_embeddings (torch.Tensor): the embeddings from the image encoder
+          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
+          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
+          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
+          multimask_output (bool): Whether to return multiple masks or a single
+            mask.
+
+        Returns:
+          torch.Tensor: batched predicted masks
+          torch.Tensor: batched predictions of mask quality
+          torch.Tensor: batched SAM token for mask output
+        """
+        masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(
+            image_embeddings=image_embeddings,
+            image_pe=image_pe,
+            sparse_prompt_embeddings=sparse_prompt_embeddings,
+            dense_prompt_embeddings=dense_prompt_embeddings,
+            repeat_image=repeat_image,
+            high_res_features=high_res_features,
+        )
+
+        # Select the correct mask or masks for output
+        if multimask_output:
+            masks = masks[:, 1:, :, :]
+            iou_pred = iou_pred[:, 1:]
+        elif self.dynamic_multimask_via_stability and not self.training:
+            masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)
+        else:
+            masks = masks[:, 0:1, :, :]
+            iou_pred = iou_pred[:, 0:1]
+
+        if multimask_output and self.use_multimask_token_for_obj_ptr:
+            sam_tokens_out = mask_tokens_out[:, 1:]  # [b, 3, c] shape
+        else:
+            # Take the mask output token. Here we *always* use the token for single mask output.
+            # At test time, even if we track after 1-click (and using multimask_output=True),
+            # we still take the single mask token here. The rationale is that we always track
+            # after multiple clicks during training, so the past tokens seen during training
+            # are always the single mask token (and we'll let it be the object-memory token).
+            sam_tokens_out = mask_tokens_out[:, 0:1]  # [b, 1, c] shape
+
+        # Prepare output
+        return masks, iou_pred, sam_tokens_out, object_score_logits
+
+    def predict_masks(
+        self,
+        image_embeddings: torch.Tensor,
+        image_pe: torch.Tensor,
+        sparse_prompt_embeddings: torch.Tensor,
+        dense_prompt_embeddings: torch.Tensor,
+        repeat_image: bool,
+        high_res_features: Optional[List[torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Predicts masks. See 'forward' for more details."""
+        # Concatenate output tokens
+        s = 0
+        if self.pred_obj_scores:
+            output_tokens = torch.cat(
+                [
+                    self.obj_score_token.weight,
+                    self.iou_token.weight,
+                    self.mask_tokens.weight,
+                ],
+                dim=0,
+            )
+            s = 1
+        else:
+            output_tokens = torch.cat(
+                [self.iou_token.weight, self.mask_tokens.weight], dim=0
+            )
+        output_tokens = output_tokens.unsqueeze(0).expand(
+            sparse_prompt_embeddings.size(0), -1, -1
+        )
+        tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+
+        # Expand per-image data in batch direction to be per-mask
+        if repeat_image:
+            src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
+        else:
+            assert image_embeddings.shape[0] == tokens.shape[0]
+            src = image_embeddings
+        src = src + dense_prompt_embeddings
+        assert image_pe.size(0) == 1, (
+            "image_pe should have size 1 in batch dim (from `get_dense_pe()`)"
+        )
+        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
+        b, c, h, w = src.shape
+
+        with torch.autograd.profiler.record_function("self.transformer"):
+            # # Run the transformer
+            src = src.to(self._src_dtype)
+            pos_src = pos_src.to(self._src_dtype)
+            tokens = tokens.to(self._src_dtype)
+            hs, new_src = self.transformer(src, pos_src, tokens)
+            # 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), :]
+
+        # Upscale mask embeddings and predict masks using the mask tokens
+        src = new_src.transpose(1, 2).view(b, c, h, w)
+        if not self.use_high_res_features:
+            upscaled_embedding = self.output_upscaling(src)
+        else:
+            with torch.autograd.profiler.record_function("upscale mask embeddings"):
+                dc1, ln1, act1, dc2, act2 = self.output_upscaling
+                feat_s0, feat_s1 = high_res_features
+                upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
+                upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)
+                # upscaled_embedding = act2(torch.cat([dc2(upscaled_embedding[:512]), dc2(upscaled_embedding[-512:])]) + feat_s0)
+
+        hyper_in_list: List[torch.Tensor] = []
+        for i in range(self.num_mask_tokens):
+            hyper_in_list.append(
+                self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
+            )
+        hyper_in = torch.stack(hyper_in_list, dim=1)
+        b, c, h, w = upscaled_embedding.shape
+        # masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
+        masks = (
+            torch.matmul(
+                upscaled_embedding.view(b, c, h * w).transpose(-1, -2),
+                hyper_in.transpose(-1, -2),
+            )
+            .transpose(-1, -2)
+            .reshape(b, -1, h, w)
+        )
+
+        # Generate mask quality predictions
+        iou_pred = self.iou_prediction_head(iou_token_out)
+        if self.pred_obj_scores:
+            assert s == 1
+            object_score_logits = self.pred_obj_score_head(hs[:, 0, :])
+        else:
+            # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1
+            object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)
+
+        return masks, iou_pred, mask_tokens_out, object_score_logits
+
+    def _get_stability_scores(self, mask_logits):
+        """
+        Compute stability scores of the mask logits based on the IoU between upper and
+        lower thresholds.
+        """
+        mask_logits = mask_logits.flatten(-2)
+        stability_delta = self.dynamic_multimask_stability_delta
+        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
+        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
+        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
+        return stability_scores
+
+    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
+        """
+        When outputting a single mask, if the stability score from the current single-mask
+        output (based on output token 0) falls below a threshold, we instead select from
+        multi-mask outputs (based on output token 1~3) the mask with the highest predicted
+        IoU score. This is intended to ensure a valid mask for both clicking and tracking.
+        """
+        # The best mask from multimask output tokens (1~3)
+        multimask_logits = all_mask_logits[:, 1:, :, :]
+        multimask_iou_scores = all_iou_scores[:, 1:]
+        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)
+        batch_inds = torch.arange(
+            multimask_iou_scores.size(0), device=all_iou_scores.device
+        )
+        best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]
+        best_multimask_logits = best_multimask_logits.unsqueeze(1)
+        best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]
+        best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)
+
+        # The mask from singlemask output token 0 and its stability score
+        singlemask_logits = all_mask_logits[:, 0:1, :, :]
+        singlemask_iou_scores = all_iou_scores[:, 0:1]
+        stability_scores = self._get_stability_scores(singlemask_logits)
+        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh
+
+        # Dynamically fall back to best multimask output upon low stability scores.
+        mask_logits_out = torch.where(
+            is_stable[..., None, None].expand_as(singlemask_logits),
+            singlemask_logits,
+            best_multimask_logits,
+        )
+        iou_scores_out = torch.where(
+            is_stable.expand_as(singlemask_iou_scores),
+            singlemask_iou_scores,
+            best_multimask_iou_scores,
+        )
+        return mask_logits_out, iou_scores_out

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam/prompt_encoder.py

@@ -0,0 +1,214 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Optional, Tuple, Type
+
+import torch
+from torch import nn
+
+from torchao._models.sam2.modeling.position_encoding import PositionEmbeddingRandom
+from torchao._models.sam2.modeling.sam2_utils import LayerNorm2d
+
+
+class PromptEncoder(nn.Module):
+    def __init__(
+        self,
+        embed_dim: int,
+        image_embedding_size: Tuple[int, int],
+        input_image_size: Tuple[int, int],
+        mask_in_chans: int,
+        activation: Type[nn.Module] = nn.GELU,
+    ) -> None:
+        """
+        Encodes prompts for input to SAM's mask decoder.
+
+        Arguments:
+          embed_dim (int): The prompts' embedding dimension
+          image_embedding_size (tuple(int, int)): The spatial size of the
+            image embedding, as (H, W).
+          input_image_size (int): The padded size of the image as input
+            to the image encoder, as (H, W).
+          mask_in_chans (int): The number of hidden channels used for
+            encoding input masks.
+          activation (nn.Module): The activation to use when encoding
+            input masks.
+        """
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.input_image_size = input_image_size
+        self.image_embedding_size = image_embedding_size
+        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
+
+        self.num_point_embeddings: int = 4  # pos/neg point + 2 box corners
+        point_embeddings = [
+            nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)
+        ]
+        self.point_embeddings = nn.ModuleList(point_embeddings)
+        self.not_a_point_embed = nn.Embedding(1, embed_dim)
+
+        self.mask_input_size = (
+            4 * image_embedding_size[0],
+            4 * image_embedding_size[1],
+        )
+        self.mask_downscaling = nn.Sequential(
+            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans // 4),
+            activation(),
+            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
+            LayerNorm2d(mask_in_chans),
+            activation(),
+            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
+        )
+        self.no_mask_embed = nn.Embedding(1, embed_dim)
+
+    def get_dense_pe(self) -> torch.Tensor:
+        """
+        Returns the positional encoding used to encode point prompts,
+        applied to a dense set of points the shape of the image encoding.
+
+        Returns:
+          torch.Tensor: Positional encoding with shape
+            1x(embed_dim)x(embedding_h)x(embedding_w)
+        """
+        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
+
+    def _embed_points(
+        self,
+        points: torch.Tensor,
+        labels: torch.Tensor,
+        pad: bool,
+    ) -> torch.Tensor:
+        """Embeds point prompts."""
+        points = points + 0.5  # Shift to center of pixel
+        if pad:
+            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
+            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
+            points = torch.cat([points, padding_point], dim=1)
+            labels = torch.cat([labels, padding_label], dim=1)
+        point_embedding = self.pe_layer.forward_with_coords(
+            points, self.input_image_size
+        )
+        # point_embedding[labels == -1] = 0.0
+        # point_embedding[labels == -1] += self.not_a_point_embed.weight
+        # point_embedding[labels == 0] += self.point_embeddings[0].weight
+        # point_embedding[labels == 1] += self.point_embeddings[1].weight
+        # point_embedding[labels == 2] += self.point_embeddings[2].weight
+        # point_embedding[labels == 3] += self.point_embeddings[3].weight
+
+        point_embedding = torch.where(
+            (labels == -1).unsqueeze(-1).expand_as(point_embedding),
+            torch.zeros_like(point_embedding) + self.not_a_point_embed.weight,
+            point_embedding,
+        )
+        point_embedding = torch.where(
+            (labels == 0).unsqueeze(-1).expand_as(point_embedding),
+            point_embedding + self.point_embeddings[0].weight,
+            point_embedding,
+        )
+        point_embedding = torch.where(
+            (labels == 1).unsqueeze(-1).expand_as(point_embedding),
+            point_embedding + self.point_embeddings[1].weight,
+            point_embedding,
+        )
+        point_embedding = torch.where(
+            (labels == 2).unsqueeze(-1).expand_as(point_embedding),
+            point_embedding + self.point_embeddings[2].weight,
+            point_embedding,
+        )
+        point_embedding = torch.where(
+            (labels == 3).unsqueeze(-1).expand_as(point_embedding),
+            point_embedding + self.point_embeddings[3].weight,
+            point_embedding,
+        )
+
+        return point_embedding
+
+    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
+        """Embeds box prompts."""
+        boxes = boxes + 0.5  # Shift to center of pixel
+        coords = boxes.reshape(-1, 2, 2)
+        corner_embedding = self.pe_layer.forward_with_coords(
+            coords, self.input_image_size
+        )
+        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
+        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
+        return corner_embedding
+
+    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
+        """Embeds mask inputs."""
+        mask_embedding = self.mask_downscaling(masks)
+        return mask_embedding
+
+    def _get_batch_size(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> int:
+        """
+        Gets the batch size of the output given the batch size of the input prompts.
+        """
+        if points is not None:
+            return points[0].shape[0]
+        elif boxes is not None:
+            return boxes.shape[0]
+        elif masks is not None:
+            return masks.shape[0]
+        else:
+            return 1
+
+    def _get_device(self) -> torch.device:
+        return self.point_embeddings[0].weight.device
+
+    def forward(
+        self,
+        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
+        boxes: Optional[torch.Tensor],
+        masks: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Embeds different types of prompts, returning both sparse and dense
+        embeddings.
+
+        Arguments:
+          points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
+            and labels to embed.
+          boxes (torch.Tensor or none): boxes to embed
+          masks (torch.Tensor or none): masks to embed
+
+        Returns:
+          torch.Tensor: sparse embeddings for the points and boxes, with shape
+            BxNx(embed_dim), where N is determined by the number of input points
+            and boxes.
+          torch.Tensor: dense embeddings for the masks, in the shape
+            Bx(embed_dim)x(embed_H)x(embed_W)
+        """
+        # if boxes is not None:
+        #     raise ValueError("Currently do not support boxes. "
+        #                      "Please create an issue on pytorch/ao.")
+        # if masks is not None:
+        #     raise ValueError("Currently do not support masks. "
+        #                      "Please create an issue on pytorch/ao.")
+        bs = self._get_batch_size(points, boxes, masks)
+        sparse_embeddings = torch.empty(
+            (bs, 0, self.embed_dim), device=self._get_device()
+        )
+        if points is not None:
+            coords, labels = points
+            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
+            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
+        if boxes is not None:
+            box_embeddings = self._embed_boxes(boxes)
+            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
+
+        if masks is not None:
+            dense_embeddings = self._embed_masks(masks)
+        else:
+            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
+                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
+            )
+
+        return sparse_embeddings, dense_embeddings

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam/transformer.py

@@ -0,0 +1,373 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import contextlib
+import math
+import warnings
+from functools import partial
+from typing import Tuple, Type
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from torchao._models.sam2.modeling.position_encoding import (
+    apply_rotary_enc,
+    compute_axial_cis,
+)
+from torchao._models.sam2.modeling.sam2_utils import MLP
+from torchao._models.sam2.utils.misc import get_sdpa_settings
+
+warnings.simplefilter(action="ignore", category=FutureWarning)
+# Check whether Flash Attention is available (and use it by default)
+OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()
+# A fallback setting to allow all available kernels if Flash Attention fails
+ALLOW_ALL_KERNELS = False
+
+
+def sdp_kernel_context(dropout_p):
+    """
+    Get the context for the attention scaled dot-product kernel. We use Flash Attention
+    by default, but fall back to all available kernels if Flash Attention fails.
+    """
+    if ALLOW_ALL_KERNELS:
+        return contextlib.nullcontext()
+
+    return torch.backends.cuda.sdp_kernel(
+        enable_flash=USE_FLASH_ATTN,
+        # if Flash attention kernel is off, then math kernel needs to be enabled
+        enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,
+        enable_mem_efficient=OLD_GPU,
+    )
+
+
+class TwoWayTransformer(nn.Module):
+    def __init__(
+        self,
+        depth: int,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+    ) -> None:
+        """
+        A transformer decoder that attends to an input image using
+        queries whose positional embedding is supplied.
+
+        Args:
+          depth (int): number of layers in the transformer
+          embedding_dim (int): the channel dimension for the input embeddings
+          num_heads (int): the number of heads for multihead attention. Must
+            divide embedding_dim
+          mlp_dim (int): the channel dimension internal to the MLP block
+          activation (nn.Module): the activation to use in the MLP block
+        """
+        super().__init__()
+        self.depth = depth
+        self.embedding_dim = embedding_dim
+        self.num_heads = num_heads
+        self.mlp_dim = mlp_dim
+        self.layers = nn.ModuleList()
+
+        for i in range(depth):
+            self.layers.append(
+                TwoWayAttentionBlock(
+                    embedding_dim=embedding_dim,
+                    num_heads=num_heads,
+                    mlp_dim=mlp_dim,
+                    activation=activation,
+                    attention_downsample_rate=attention_downsample_rate,
+                    skip_first_layer_pe=(i == 0),
+                )
+            )
+
+        self.final_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm_final_attn = nn.LayerNorm(embedding_dim)
+
+    def forward(
+        self,
+        image_embedding: Tensor,
+        image_pe: Tensor,
+        point_embedding: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          image_embedding (torch.Tensor): image to attend to. Should be shape
+            B x embedding_dim x h x w for any h and w.
+          image_pe (torch.Tensor): the positional encoding to add to the image. Must
+            have the same shape as image_embedding.
+          point_embedding (torch.Tensor): the embedding to add to the query points.
+            Must have shape B x N_points x embedding_dim for any N_points.
+
+        Returns:
+          torch.Tensor: the processed point_embedding
+          torch.Tensor: the processed image_embedding
+        """
+        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
+        bs, c, h, w = image_embedding.shape
+        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
+        image_pe = image_pe.flatten(2).permute(0, 2, 1)
+
+        # Prepare queries
+        queries = point_embedding
+        keys = image_embedding
+
+        # Apply transformer blocks and final layernorm
+        for layer in self.layers:
+            queries, keys = layer(
+                queries=queries,
+                keys=keys,
+                query_pe=point_embedding,
+                key_pe=image_pe,
+            )
+
+        # Apply the final attention layer from the points to the image
+        q = queries + point_embedding
+        k = keys + image_pe
+        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm_final_attn(queries)
+
+        return queries, keys
+
+
+class TwoWayAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        mlp_dim: int = 2048,
+        activation: Type[nn.Module] = nn.ReLU,
+        attention_downsample_rate: int = 2,
+        skip_first_layer_pe: bool = False,
+    ) -> None:
+        """
+        A transformer block with four layers: (1) self-attention of sparse
+        inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
+        block on sparse inputs, and (4) cross attention of dense inputs to sparse
+        inputs.
+
+        Arguments:
+          embedding_dim (int): the channel dimension of the embeddings
+          num_heads (int): the number of heads in the attention layers
+          mlp_dim (int): the hidden dimension of the mlp block
+          activation (nn.Module): the activation of the mlp block
+          skip_first_layer_pe (bool): skip the PE on the first layer
+        """
+        super().__init__()
+        self.self_attn = Attention(embedding_dim, num_heads)
+        self.norm1 = nn.LayerNorm(embedding_dim)
+
+        self.cross_attn_token_to_image = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+        self.norm2 = nn.LayerNorm(embedding_dim)
+
+        self.mlp = MLP(
+            embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation
+        )
+        self.norm3 = nn.LayerNorm(embedding_dim)
+
+        self.norm4 = nn.LayerNorm(embedding_dim)
+        self.cross_attn_image_to_token = Attention(
+            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
+        )
+
+        self.skip_first_layer_pe = skip_first_layer_pe
+
+    def forward(
+        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
+    ) -> Tuple[Tensor, Tensor]:
+        # Self attention block
+        if self.skip_first_layer_pe:
+            queries = self.self_attn(q=queries, k=queries, v=queries)
+        else:
+            q = queries + query_pe
+            attn_out = self.self_attn(q=q, k=q, v=queries)
+            queries = queries + attn_out
+        queries = self.norm1(queries)
+
+        # Cross attention block, tokens attending to image embedding
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
+        queries = queries + attn_out
+        queries = self.norm2(queries)
+
+        # MLP block
+        mlp_out = self.mlp(queries)
+        queries = queries + mlp_out
+        queries = self.norm3(queries)
+
+        # Cross attention block, image embedding attending to tokens
+        q = queries + query_pe
+        k = keys + key_pe
+        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
+        keys = keys + attn_out
+        keys = self.norm4(keys)
+
+        return queries, keys
+
+
+class Attention(nn.Module):
+    """
+    An attention layer that allows for downscaling the size of the embedding
+    after projection to queries, keys, and values.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_heads: int,
+        downsample_rate: int = 1,
+        dropout: float = 0.0,
+        kv_in_dim: int = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
+        self.internal_dim = embedding_dim // downsample_rate
+        self.num_heads = num_heads
+        assert self.internal_dim % num_heads == 0, (
+            "num_heads must divide embedding_dim."
+        )
+
+        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
+        self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+        self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
+        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
+
+        self.dropout_p = dropout
+
+    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
+        b, n, c = x.shape
+        x = x.reshape(b, n, num_heads, c // num_heads)
+        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head
+
+    def _recombine_heads(self, x: Tensor) -> Tensor:
+        b, n_heads, n_tokens, c_per_head = x.shape
+        x = x.transpose(1, 2)
+        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        dropout_p = self.dropout_p if self.training else 0.0
+        # # Attention
+        # try:
+        #     with sdp_kernel_context(dropout_p):
+        #         out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+        # except Exception as e:
+        #     # Fall back to all kernels if the Flash attention kernel fails
+        #     warnings.warn(
+        #         f"Flash Attention kernel failed due to: {e}\nFalling back to all available "
+        #         f"kernels for scaled_dot_product_attention (which may have a slower speed).",
+        #         category=UserWarning,
+        #         stacklevel=2,
+        #     )
+        #     global ALLOW_ALL_KERNELS
+        #     ALLOW_ALL_KERNELS = True
+        #     out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+        # TODO: This scale should not be needed. But without it compile causes a NaN.
+        out = F.scaled_dot_product_attention(
+            q, k, v, dropout_p=dropout_p, scale=(1.0 / math.sqrt(q.size(-1)))
+        )
+
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out
+
+
+class RoPEAttention(Attention):
+    """Attention with rotary position encoding."""
+
+    def __init__(
+        self,
+        *args,
+        rope_theta=10000.0,
+        # whether to repeat q rope to match k length
+        # this is needed for cross-attention to memories
+        rope_k_repeat=False,
+        feat_sizes=(32, 32),  # [w, h] for stride 16 feats at 512 resolution
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.compute_cis = partial(
+            compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta
+        )
+        freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])
+        self.freqs_cis = freqs_cis
+        self.rope_k_repeat = rope_k_repeat
+
+    def forward(
+        self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0
+    ) -> Tensor:
+        # Input projections
+        q = self.q_proj(q)
+        k = self.k_proj(k)
+        v = self.v_proj(v)
+
+        # Separate into heads
+        q = self._separate_heads(q, self.num_heads)
+        k = self._separate_heads(k, self.num_heads)
+        v = self._separate_heads(v, self.num_heads)
+
+        # Apply rotary position encoding
+        w = h = math.sqrt(q.shape[-2])
+        # NOTE: Disabling this.
+        # self.freqs_cis = self.freqs_cis.to(q.device)
+        if self.freqs_cis.shape[0] != q.shape[-2]:
+            self.freqs_cis = self.compute_cis(
+                end_x=w, end_y=h, device=q.device
+            )  # .to(q.device)
+        if q.shape[-2] != k.shape[-2]:
+            assert self.rope_k_repeat
+
+        num_k_rope = k.size(-2) - num_k_exclude_rope
+        q, k[:, :, :num_k_rope] = apply_rotary_enc(
+            q,
+            k[:, :, :num_k_rope],
+            freqs_cis=self.freqs_cis,
+            repeat_freqs_k=self.rope_k_repeat,
+        )
+
+        dropout_p = self.dropout_p if self.training else 0.0
+        # # Attention
+        # try:
+        #     with sdp_kernel_context(dropout_p):
+        #         out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
+        # except Exception as e:
+        #     # Fall back to all kernels if the Flash attention kernel fails
+        #     warnings.warn(
+        #         f"Flash Attention kernel failed due to: {e}\nFalling back to all available "
+        #         f"kernels for scaled_dot_product_attention (which may have a slower speed).",
+        #         category=UserWarning,
+        #         stacklevel=2,
+        #     )
+        #     global ALLOW_ALL_KERNELS
+        #     ALLOW_ALL_KERNELS = True
+        # TODO: This scale should not be needed. But without it compile causes a NaN.
+        out = F.scaled_dot_product_attention(
+            q, k, v, dropout_p=dropout_p, scale=(1.0 / math.sqrt(q.size(-1)))
+        )
+
+        out = self._recombine_heads(out)
+        out = self.out_proj(out)
+
+        return out

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam2_base.py

@@ -0,0 +1,929 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed
+import torch.nn.functional as F
+from torch.nn.init import trunc_normal_
+
+from torchao._models.sam2.modeling.sam.mask_decoder import MaskDecoder
+from torchao._models.sam2.modeling.sam.prompt_encoder import PromptEncoder
+from torchao._models.sam2.modeling.sam.transformer import TwoWayTransformer
+from torchao._models.sam2.modeling.sam2_utils import (
+    MLP,
+    get_1d_sine_pe,
+    select_closest_cond_frames,
+)
+
+# a large negative value as a placeholder score for missing objects
+NO_OBJ_SCORE = -1024.0
+
+
+class SAM2Base(torch.nn.Module):
+    def __init__(
+        self,
+        image_encoder,
+        memory_attention,
+        memory_encoder,
+        num_maskmem=7,  # default 1 input frame + 6 previous frames
+        image_size=512,
+        backbone_stride=16,  # stride of the image backbone output
+        sigmoid_scale_for_mem_enc=1.0,  # scale factor for mask sigmoid prob
+        sigmoid_bias_for_mem_enc=0.0,  # bias factor for mask sigmoid prob
+        # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks
+        binarize_mask_from_pts_for_mem_enc=False,
+        use_mask_input_as_output_without_sam=False,  # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder
+        # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,
+        # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model
+        # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.
+        max_cond_frames_in_attn=-1,
+        # on the first frame, whether to directly add the no-memory embedding to the image feature
+        # (instead of using the transformer encoder)
+        directly_add_no_mem_embed=False,
+        # whether to use high-resolution feature maps in the SAM mask decoder
+        use_high_res_features_in_sam=False,
+        # whether to output multiple (3) masks for the first click on initial conditioning frames
+        multimask_output_in_sam=False,
+        # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;
+        # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)
+        multimask_min_pt_num=1,
+        multimask_max_pt_num=1,
+        # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)
+        multimask_output_for_tracking=False,
+        # Whether to use multimask tokens for obj ptr; Only relevant when both
+        # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True
+        use_multimask_token_for_obj_ptr: bool = False,
+        # whether to use sigmoid to restrict ious prediction to [0-1]
+        iou_prediction_use_sigmoid=False,
+        # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).
+        # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of
+        # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.
+        memory_temporal_stride_for_eval=1,
+        # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)
+        non_overlap_masks_for_mem_enc=False,
+        # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+        use_obj_ptrs_in_encoder=False,
+        # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`)
+        max_obj_ptrs_in_encoder=16,
+        # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`)
+        add_tpos_enc_to_obj_ptrs=True,
+        # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference
+        # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)
+        proj_tpos_enc_in_obj_ptrs=False,
+        # whether to use signed distance (instead of unsigned absolute distance) in the temporal positional encoding in the object pointers
+        # (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)
+        use_signed_tpos_enc_to_obj_ptrs=False,
+        # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation
+        # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking)
+        only_obj_ptrs_in_the_past_for_eval=False,
+        # Whether to predict if there is an object in the frame
+        pred_obj_scores: bool = False,
+        # Whether to use an MLP to predict object scores
+        pred_obj_scores_mlp: bool = False,
+        # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True;
+        # Whether to have a fixed no obj pointer when there is no object present
+        # or to use it as an additive embedding with obj_ptr produced by decoder
+        fixed_no_obj_ptr: bool = False,
+        # Soft no object, i.e. mix in no_obj_ptr softly,
+        # hope to make recovery easier if there is a mistake and mitigate accumulation of errors
+        soft_no_obj_ptr: bool = False,
+        use_mlp_for_obj_ptr_proj: bool = False,
+        # add no obj embedding to spatial frames
+        no_obj_embed_spatial: bool = False,
+        # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.
+        sam_mask_decoder_extra_args=None,
+        compile_image_encoder: bool = False,
+    ):
+        super().__init__()
+
+        # Part 1: the image backbone
+        self.image_encoder = image_encoder
+        # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting
+        self.use_high_res_features_in_sam = use_high_res_features_in_sam
+        self.num_feature_levels = 3 if use_high_res_features_in_sam else 1
+        self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder
+        self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder
+        if use_obj_ptrs_in_encoder:
+            # A conv layer to downsample the mask prompt to stride 4 (the same stride as
+            # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,
+            # so that it can be fed into the SAM mask decoder to generate a pointer.
+            self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)
+        self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs
+        if proj_tpos_enc_in_obj_ptrs:
+            assert add_tpos_enc_to_obj_ptrs  # these options need to be used together
+        self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs
+        self.use_signed_tpos_enc_to_obj_ptrs = use_signed_tpos_enc_to_obj_ptrs
+        self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval
+
+        # Part 2: memory attention to condition current frame's visual features
+        # with memories (and obj ptrs) from past frames
+        self.memory_attention = memory_attention
+        self.hidden_dim = image_encoder.neck.d_model
+
+        # Part 3: memory encoder for the previous frame's outputs
+        self.memory_encoder = memory_encoder
+        self.mem_dim = self.hidden_dim
+        if hasattr(self.memory_encoder, "out_proj") and hasattr(
+            self.memory_encoder.out_proj, "weight"
+        ):
+            # if there is compression of memories along channel dim
+            self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]
+        self.num_maskmem = num_maskmem  # Number of memories accessible
+        # Temporal encoding of the memories
+        self.maskmem_tpos_enc = torch.nn.Parameter(
+            torch.zeros(num_maskmem, 1, 1, self.mem_dim)
+        )
+        trunc_normal_(self.maskmem_tpos_enc, std=0.02)
+        # a single token to indicate no memory embedding from previous frames
+        self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+        self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
+        trunc_normal_(self.no_mem_embed, std=0.02)
+        trunc_normal_(self.no_mem_pos_enc, std=0.02)
+        self.directly_add_no_mem_embed = directly_add_no_mem_embed
+        # Apply sigmoid to the output raw mask logits (to turn them from
+        # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder
+        self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc
+        self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc
+        self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc
+        self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc
+        self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval
+        # On frames with mask input, whether to directly output the input mask without
+        # using a SAM prompt encoder + mask decoder
+        self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam
+        self.multimask_output_in_sam = multimask_output_in_sam
+        self.multimask_min_pt_num = multimask_min_pt_num
+        self.multimask_max_pt_num = multimask_max_pt_num
+        self.multimask_output_for_tracking = multimask_output_for_tracking
+        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
+        self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid
+
+        # Part 4: SAM-style prompt encoder (for both mask and point inputs)
+        # and SAM-style mask decoder for the final mask output
+        self.image_size = image_size
+        self.backbone_stride = backbone_stride
+        self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args
+        self.pred_obj_scores = pred_obj_scores
+        self.pred_obj_scores_mlp = pred_obj_scores_mlp
+        self.fixed_no_obj_ptr = fixed_no_obj_ptr
+        self.soft_no_obj_ptr = soft_no_obj_ptr
+        if self.fixed_no_obj_ptr:
+            assert self.pred_obj_scores
+            assert self.use_obj_ptrs_in_encoder
+        if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:
+            self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))
+            trunc_normal_(self.no_obj_ptr, std=0.02)
+        self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj
+        self.no_obj_embed_spatial = None
+        if no_obj_embed_spatial:
+            self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim))
+            trunc_normal_(self.no_obj_embed_spatial, std=0.02)
+
+        self._build_sam_heads()
+        self.max_cond_frames_in_attn = max_cond_frames_in_attn
+
+        # Model compilation
+        if compile_image_encoder:
+            # Compile the forward function (not the full module) to allow loading checkpoints.
+            print(
+                "Image encoder compilation is enabled. First forward pass will be slow."
+            )
+            self.image_encoder.forward = torch.compile(
+                self.image_encoder.forward,
+                mode="max-autotune",
+                fullgraph=True,
+                dynamic=False,
+            )
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError(
+            "Please use the corresponding methods in SAM2VideoPredictor for inference or SAM2Train for training/fine-tuning"
+            "See notebooks/video_predictor_example.ipynb for an inference example."
+        )
+
+    def _build_sam_heads(self):
+        """Build SAM-style prompt encoder and mask decoder."""
+        self.sam_prompt_embed_dim = self.hidden_dim
+        self.sam_image_embedding_size = self.image_size // self.backbone_stride
+
+        # build PromptEncoder and MaskDecoder from SAM
+        # (their hyperparameters like `mask_in_chans=16` are from SAM code)
+        self.sam_prompt_encoder = PromptEncoder(
+            embed_dim=self.sam_prompt_embed_dim,
+            image_embedding_size=(
+                self.sam_image_embedding_size,
+                self.sam_image_embedding_size,
+            ),
+            input_image_size=(self.image_size, self.image_size),
+            mask_in_chans=16,
+        )
+        self.sam_mask_decoder = MaskDecoder(
+            num_multimask_outputs=3,
+            transformer=TwoWayTransformer(
+                depth=2,
+                embedding_dim=self.sam_prompt_embed_dim,
+                mlp_dim=2048,
+                num_heads=8,
+            ),
+            transformer_dim=self.sam_prompt_embed_dim,
+            iou_head_depth=3,
+            iou_head_hidden_dim=256,
+            use_high_res_features=self.use_high_res_features_in_sam,
+            iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid,
+            pred_obj_scores=self.pred_obj_scores,
+            pred_obj_scores_mlp=self.pred_obj_scores_mlp,
+            use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr,
+            **(self.sam_mask_decoder_extra_args or {}),
+        )
+        if self.use_obj_ptrs_in_encoder:
+            # a linear projection on SAM output tokens to turn them into object pointers
+            self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)
+            if self.use_mlp_for_obj_ptr_proj:
+                self.obj_ptr_proj = MLP(
+                    self.hidden_dim, self.hidden_dim, self.hidden_dim, 3
+                )
+        else:
+            self.obj_ptr_proj = torch.nn.Identity()
+        if self.proj_tpos_enc_in_obj_ptrs:
+            # a linear projection on temporal positional encoding in object pointers to
+            # avoid potential interference with spatial positional encoding
+            self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)
+        else:
+            self.obj_ptr_tpos_proj = torch.nn.Identity()
+
+    def _forward_sam_heads(
+        self,
+        backbone_features,
+        point_inputs=None,
+        mask_inputs=None,
+        high_res_features=None,
+        multimask_output=False,
+    ):
+        """
+        Forward SAM prompt encoders and mask heads.
+
+        Inputs:
+        - backbone_features: image features of [B, C, H, W] shape
+        - point_inputs: a dictionary with "point_coords" and "point_labels", where
+          1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the
+             absolute pixel-unit coordinate in (x, y) format of the P input points
+          2) "point_labels" has shape [B, P] and int32 dtype, where 1 means
+             positive clicks, 0 means negative clicks, and -1 means padding
+        - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the
+          same spatial size as the image.
+        - high_res_features: either 1) None or 2) or a list of length 2 containing
+          two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,
+          which will be used as high-resolution feature maps for SAM decoder.
+        - multimask_output: if it's True, we output 3 candidate masks and their 3
+          corresponding IoU estimates, and if it's False, we output only 1 mask and
+          its corresponding IoU estimate.
+
+        Outputs:
+        - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if
+          `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM
+          output mask logits (before sigmoid) for the low-resolution masks, with 4x
+          the resolution (1/4 stride) of the input backbone_features.
+        - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3
+          if `multimask_output=True` and M = 1 if `multimask_output=False`),
+          upsampled from the low-resolution masks, with shape size as the image
+          (stride is 1 pixel).
+        - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1
+          if `multimask_output=False`), the estimated IoU of each output mask.
+        - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.
+          If `multimask_output=True`, it's the mask with the highest IoU estimate.
+          If `multimask_output=False`, it's the same as `low_res_multimasks`.
+        - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.
+          If `multimask_output=True`, it's the mask with the highest IoU estimate.
+          If `multimask_output=False`, it's the same as `high_res_multimasks`.
+        - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted
+          based on the output token from the SAM mask decoder.
+        """
+        B = backbone_features.size(0)
+        device = backbone_features.device
+        assert backbone_features.size(1) == self.sam_prompt_embed_dim
+        assert backbone_features.size(2) == self.sam_image_embedding_size
+        assert backbone_features.size(3) == self.sam_image_embedding_size
+
+        # a) Handle point prompts
+        if point_inputs is not None:
+            sam_point_coords = point_inputs["point_coords"]
+            sam_point_labels = point_inputs["point_labels"]
+            assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B
+        else:
+            # If no points are provide, pad with an empty point (with label -1)
+            sam_point_coords = torch.zeros(B, 1, 2, device=device)
+            sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)
+
+        # b) Handle mask prompts
+        if mask_inputs is not None:
+            # If mask_inputs is provided, downsize it into low-res mask input if needed
+            # and feed it as a dense mask prompt into the SAM mask encoder
+            assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)
+            if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:
+                sam_mask_prompt = F.interpolate(
+                    mask_inputs.float(),
+                    size=self.sam_prompt_encoder.mask_input_size,
+                    align_corners=False,
+                    mode="bilinear",
+                    antialias=True,  # use antialias for downsampling
+                )
+            else:
+                sam_mask_prompt = mask_inputs
+        else:
+            # Otherwise, simply feed None (and SAM's prompt encoder will add
+            # a learned `no_mask_embed` to indicate no mask input in this case).
+            sam_mask_prompt = None
+
+        sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(
+            points=(sam_point_coords, sam_point_labels),
+            boxes=None,
+            masks=sam_mask_prompt,
+        )
+        (
+            low_res_multimasks,
+            ious,
+            sam_output_tokens,
+            object_score_logits,
+        ) = self.sam_mask_decoder(
+            image_embeddings=backbone_features,
+            image_pe=self.sam_prompt_encoder.get_dense_pe(),
+            sparse_prompt_embeddings=sparse_embeddings,
+            dense_prompt_embeddings=dense_embeddings,
+            multimask_output=multimask_output,
+            repeat_image=False,  # the image is already batched
+            high_res_features=high_res_features,
+        )
+        if self.pred_obj_scores:
+            is_obj_appearing = object_score_logits > 0
+
+            # Mask used for spatial memories is always a *hard* choice between obj and no obj,
+            # consistent with the actual mask prediction
+            low_res_multimasks = torch.where(
+                is_obj_appearing[:, None, None],
+                low_res_multimasks,
+                NO_OBJ_SCORE,
+            )
+
+        # convert masks from possibly bfloat16 (or float16) to float32
+        # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)
+        low_res_multimasks = low_res_multimasks.float()
+        high_res_multimasks = F.interpolate(
+            low_res_multimasks,
+            size=(self.image_size, self.image_size),
+            mode="bilinear",
+            align_corners=False,
+        )
+
+        sam_output_token = sam_output_tokens[:, 0]
+        if multimask_output:
+            # take the best mask prediction (with the highest IoU estimation)
+            best_iou_inds = torch.argmax(ious, dim=-1)
+            batch_inds = torch.arange(B, device=device)
+            low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+            high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)
+            if sam_output_tokens.size(1) > 1:
+                sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]
+        else:
+            low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks
+
+        # Extract object pointer from the SAM output token (with occlusion handling)
+        obj_ptr = self.obj_ptr_proj(sam_output_token)
+        if self.pred_obj_scores:
+            # Allow *soft* no obj ptr, unlike for masks
+            if self.soft_no_obj_ptr:
+                lambda_is_obj_appearing = object_score_logits.sigmoid()
+            else:
+                lambda_is_obj_appearing = is_obj_appearing.float()
+
+            if self.fixed_no_obj_ptr:
+                obj_ptr = lambda_is_obj_appearing * obj_ptr
+            obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+        return (
+            low_res_multimasks,
+            high_res_multimasks,
+            ious,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        )
+
+    def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):
+        """
+        Directly turn binary `mask_inputs` into a output mask logits without using SAM.
+        (same input and output shapes as in _forward_sam_heads above).
+        """
+        # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).
+        out_scale, out_bias = 20.0, -10.0  # sigmoid(-10.0)=4.5398e-05
+        mask_inputs_float = mask_inputs.float()
+        high_res_masks = mask_inputs_float * out_scale + out_bias
+        low_res_masks = F.interpolate(
+            high_res_masks,
+            size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4),
+            align_corners=False,
+            mode="bilinear",
+            antialias=True,  # use antialias for downsampling
+        )
+        # a dummy IoU prediction of all 1's under mask input
+        ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()
+        if not self.use_obj_ptrs_in_encoder:
+            # all zeros as a dummy object pointer (of shape [B, C])
+            obj_ptr = torch.zeros(
+                mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device
+            )
+        else:
+            # produce an object pointer using the SAM decoder from the mask input
+            _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(
+                backbone_features=backbone_features,
+                mask_inputs=self.mask_downsample(mask_inputs_float),
+                high_res_features=high_res_features,
+            )
+        # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;
+        # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying
+        # on the object_scores from the SAM decoder.
+        is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)
+        is_obj_appearing = is_obj_appearing[..., None]
+        lambda_is_obj_appearing = is_obj_appearing.float()
+        object_score_logits = out_scale * lambda_is_obj_appearing + out_bias
+        if self.pred_obj_scores:
+            if self.fixed_no_obj_ptr:
+                obj_ptr = lambda_is_obj_appearing * obj_ptr
+            obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr
+
+        return (
+            low_res_masks,
+            high_res_masks,
+            ious,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        )
+
+    def forward_image(self, img_batch: torch.Tensor):
+        """Get the image feature on the input batch."""
+        backbone_out = self.image_encoder(img_batch)
+        if self.use_high_res_features_in_sam:
+            # precompute projected level 0 and level 1 features in SAM decoder
+            # to avoid running it again on every SAM click
+            # NOTE: These are 1x1 convolutions
+            backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0(
+                backbone_out["backbone_fpn"][0]
+            )
+            backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1(
+                backbone_out["backbone_fpn"][1]
+            )
+        return backbone_out
+
+    def _prepare_backbone_features(self, backbone_out):
+        """Prepare and flatten visual features."""
+        backbone_out = backbone_out.copy()
+        assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"])
+        assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels
+
+        feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :]
+        vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :]
+
+        feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]
+        # flatten NxCxHxW to HWxNxC
+        vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]
+        vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]
+
+        return backbone_out, vision_feats, vision_pos_embeds, feat_sizes
+
+    def _prepare_memory_conditioned_features(
+        self,
+        frame_idx,
+        is_init_cond_frame,
+        current_vision_feats,
+        current_vision_pos_embeds,
+        feat_sizes,
+        output_dict,
+        num_frames,
+        track_in_reverse=False,  # tracking in reverse time order (for demo usage)
+    ):
+        """Fuse the current frame's visual feature map with previous memory."""
+        B = current_vision_feats[-1].size(1)  # batch size on this frame
+        C = self.hidden_dim
+        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size
+        device = current_vision_feats[-1].device
+        # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.
+        # In this case, we skip the fusion with any memory.
+        if self.num_maskmem == 0:  # Disable memory and skip fusion
+            pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+            return pix_feat
+
+        num_obj_ptr_tokens = 0
+        tpos_sign_mul = -1 if track_in_reverse else 1
+        # Step 1: condition the visual features of the current frame on previous memories
+        if not is_init_cond_frame:
+            # Retrieve the memories encoded with the maskmem backbone
+            to_cat_memory, to_cat_memory_pos_embed = [], []
+            # Add conditioning frames's output first (all cond frames have t_pos=0 for
+            # when getting temporal positional embedding below)
+            assert len(output_dict["cond_frame_outputs"]) > 0
+            # Select a maximum number of temporally closest cond frames for cross attention
+            cond_outputs = output_dict["cond_frame_outputs"]
+            selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(
+                frame_idx, cond_outputs, self.max_cond_frames_in_attn
+            )
+            t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()]
+            # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory
+            # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1
+            # We also allow taking the memory frame non-consecutively (with stride>1), in which case
+            # we take (self.num_maskmem - 2) frames among every stride-th frames plus the last frame.
+            stride = 1 if self.training else self.memory_temporal_stride_for_eval
+            for t_pos in range(1, self.num_maskmem):
+                t_rel = self.num_maskmem - t_pos  # how many frames before current frame
+                if t_rel == 1:
+                    # for t_rel == 1, we take the last frame (regardless of r)
+                    if not track_in_reverse:
+                        # the frame immediately before this frame (i.e. frame_idx - 1)
+                        prev_frame_idx = frame_idx - t_rel
+                    else:
+                        # the frame immediately after this frame (i.e. frame_idx + 1)
+                        prev_frame_idx = frame_idx + t_rel
+                else:
+                    # for t_rel >= 2, we take the memory frame from every r-th frames
+                    if not track_in_reverse:
+                        # first find the nearest frame among every r-th frames before this frame
+                        # for r=1, this would be (frame_idx - 2)
+                        prev_frame_idx = ((frame_idx - 2) // stride) * stride
+                        # then seek further among every r-th frames
+                        prev_frame_idx = prev_frame_idx - (t_rel - 2) * stride
+                    else:
+                        # first find the nearest frame among every r-th frames after this frame
+                        # for r=1, this would be (frame_idx + 2)
+                        prev_frame_idx = -(-(frame_idx + 2) // stride) * stride
+                        # then seek further among every r-th frames
+                        prev_frame_idx = prev_frame_idx + (t_rel - 2) * stride
+                out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None)
+                if out is None:
+                    # If an unselected conditioning frame is among the last (self.num_maskmem - 1)
+                    # frames, we still attend to it as if it's a non-conditioning frame.
+                    out = unselected_cond_outputs.get(prev_frame_idx, None)
+                t_pos_and_prevs.append((t_pos, out))
+
+            for t_pos, prev in t_pos_and_prevs:
+                if prev is None:
+                    continue  # skip padding frames
+                # "maskmem_features" might have been offloaded to CPU in demo use cases,
+                # so we load it back to GPU (it's a no-op if it's already on GPU).
+                feats = prev["maskmem_features"].to(device, non_blocking=True)
+                to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))
+                # Spatial positional encoding (it might have been offloaded to CPU in eval)
+                maskmem_enc = prev["maskmem_pos_enc"][-1].to(device)
+                maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)
+                # Temporal positional encoding
+                maskmem_enc = (
+                    maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]
+                )
+                to_cat_memory_pos_embed.append(maskmem_enc)
+
+            # Construct the list of past object pointers
+            if self.use_obj_ptrs_in_encoder:
+                max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)
+                # First add those object pointers from selected conditioning frames
+                # (optionally, only include object pointers in the past during evaluation)
+                if not self.training and self.only_obj_ptrs_in_the_past_for_eval:
+                    ptr_cond_outputs = {
+                        t: out
+                        for t, out in selected_cond_outputs.items()
+                        if (t >= frame_idx if track_in_reverse else t <= frame_idx)
+                    }
+                else:
+                    ptr_cond_outputs = selected_cond_outputs
+                pos_and_ptrs = [
+                    # Temporal pos encoding contains how far away each pointer is from current frame
+                    (
+                        (
+                            (frame_idx - t) * tpos_sign_mul
+                            if self.use_signed_tpos_enc_to_obj_ptrs
+                            else abs(frame_idx - t)
+                        ),
+                        out["obj_ptr"],
+                    )
+                    for t, out in ptr_cond_outputs.items()
+                ]
+                # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame
+                for t_diff in range(1, max_obj_ptrs_in_encoder):
+                    t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff
+                    if t < 0 or (num_frames is not None and t >= num_frames):
+                        break
+                    out = output_dict["non_cond_frame_outputs"].get(
+                        t, unselected_cond_outputs.get(t, None)
+                    )
+                    if out is not None:
+                        pos_and_ptrs.append((t_diff, out["obj_ptr"]))
+                # If we have at least one object pointer, add them to the across attention
+                if len(pos_and_ptrs) > 0:
+                    pos_list, ptrs_list = zip(*pos_and_ptrs)
+                    # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape
+                    obj_ptrs = torch.stack(ptrs_list, dim=0)
+                    # a temporal positional embedding based on how far each object pointer is from
+                    # the current frame (sine embedding normalized by the max pointer num).
+                    if self.add_tpos_enc_to_obj_ptrs:
+                        t_diff_max = max_obj_ptrs_in_encoder - 1
+                        tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim
+                        obj_pos = (
+                            torch.tensor(pos_list)
+                            .pin_memory()
+                            .to(device=device, non_blocking=True)
+                        )
+                        obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim)
+                        obj_pos = self.obj_ptr_tpos_proj(obj_pos)
+                        obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim)
+                    else:
+                        obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim)
+                    if self.mem_dim < C:
+                        # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C
+                        obj_ptrs = obj_ptrs.reshape(
+                            -1, B, C // self.mem_dim, self.mem_dim
+                        )
+                        obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)
+                        obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)
+                    to_cat_memory.append(obj_ptrs)
+                    to_cat_memory_pos_embed.append(obj_pos)
+                    num_obj_ptr_tokens = obj_ptrs.shape[0]
+                else:
+                    num_obj_ptr_tokens = 0
+        else:
+            # for initial conditioning frames, encode them without using any previous memory
+            if self.directly_add_no_mem_embed:
+                # directly add no-mem embedding (instead of using the transformer encoder)
+                pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed
+                pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
+                return pix_feat_with_mem
+
+            # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder)
+            to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)]
+            to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]
+
+        # Step 2: Concatenate the memories and forward through the transformer encoder
+        memory = torch.cat(to_cat_memory, dim=0)
+        memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0)
+
+        current_vision_feats = [c.clone() for c in current_vision_feats]
+        current_vision_pos_embeds = [c.clone() for c in current_vision_pos_embeds]
+        memory = memory.clone()
+        memory_pos_embed = memory_pos_embed.clone()
+        pix_feat_with_mem = self.memory_attention(
+            curr=current_vision_feats,
+            curr_pos=current_vision_pos_embeds,
+            memory=memory,
+            memory_pos=memory_pos_embed,
+            num_obj_ptr_tokens=num_obj_ptr_tokens,
+        )
+        pix_feat_with_mem = pix_feat_with_mem.clone()
+        # reshape the output (HW)BC => BCHW
+        pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)
+        return pix_feat_with_mem
+
+    def _encode_new_memory(
+        self,
+        current_vision_feats,
+        feat_sizes,
+        pred_masks_high_res,
+        object_score_logits,
+        is_mask_from_pts,
+    ):
+        """Encode the current image and its prediction into a memory feature."""
+        B = current_vision_feats[-1].size(1)  # batch size on this frame
+        C = self.hidden_dim
+        H, W = feat_sizes[-1]  # top-level (lowest-resolution) feature size
+        # top-level feature, (HW)BC => BCHW
+        pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
+        if self.non_overlap_masks_for_mem_enc and not self.training:
+            # optionally, apply non-overlapping constraints to the masks (it's applied
+            # in the batch dimension and should only be used during eval, where all
+            # the objects come from the same video under batch size 1).
+            pred_masks_high_res = self._apply_non_overlapping_constraints(
+                pred_masks_high_res
+            )
+        # scale the raw mask logits with a temperature before applying sigmoid
+        binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts
+        if binarize and not self.training:
+            mask_for_mem = (pred_masks_high_res > 0).float()
+        else:
+            # apply sigmoid on the raw mask logits to turn them into range (0, 1)
+            mask_for_mem = torch.sigmoid(pred_masks_high_res)
+        # apply scale and bias terms to the sigmoid probabilities
+        if self.sigmoid_scale_for_mem_enc != 1.0:
+            mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc
+        if self.sigmoid_bias_for_mem_enc != 0.0:
+            mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc
+        maskmem_out = self.memory_encoder(
+            pix_feat,
+            mask_for_mem,
+            skip_mask_sigmoid=True,  # sigmoid already applied
+        )
+        maskmem_features = maskmem_out["vision_features"].clone()
+        maskmem_pos_enc = [m.clone() for m in maskmem_out["vision_pos_enc"]]
+        # add a no-object embedding to the spatial memory to indicate that the frame
+        # is predicted to be occluded (i.e. no object is appearing in the frame)
+        if self.no_obj_embed_spatial is not None:
+            is_obj_appearing = (object_score_logits > 0).float()
+            maskmem_features += (
+                1 - is_obj_appearing[..., None, None]
+            ) * self.no_obj_embed_spatial[..., None, None].expand(
+                *maskmem_features.shape
+            )
+
+        return maskmem_features, maskmem_pos_enc
+
+    def _track_step(
+        self,
+        frame_idx,
+        is_init_cond_frame,
+        current_vision_feats,
+        current_vision_pos_embeds,
+        feat_sizes,
+        point_inputs,
+        mask_inputs,
+        output_dict,
+        num_frames,
+        track_in_reverse,
+        prev_sam_mask_logits,
+    ):
+        current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs}
+        # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW
+        if len(current_vision_feats) > 1:
+            high_res_features = [
+                x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)
+                for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])
+            ]
+        else:
+            high_res_features = None
+        if mask_inputs is not None and self.use_mask_input_as_output_without_sam:
+            # When use_mask_input_as_output_without_sam=True, we directly output the mask input
+            # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.
+            pix_feat = current_vision_feats[-1].permute(1, 2, 0)
+            pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])
+            sam_outputs = self._use_mask_as_output(
+                pix_feat, high_res_features, mask_inputs
+            )
+        else:
+            # fused the visual feature with previous memory features in the memory bank
+            pix_feat = self._prepare_memory_conditioned_features(
+                frame_idx=frame_idx,
+                is_init_cond_frame=is_init_cond_frame,
+                current_vision_feats=current_vision_feats[-1:],
+                current_vision_pos_embeds=current_vision_pos_embeds[-1:],
+                feat_sizes=feat_sizes[-1:],
+                output_dict=output_dict,
+                num_frames=num_frames,
+                track_in_reverse=track_in_reverse,
+            )
+            # apply SAM-style segmentation head
+            # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,
+            # e.g. in demo where such logits come from earlier interaction instead of correction sampling
+            # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)
+            if prev_sam_mask_logits is not None:
+                assert point_inputs is not None and mask_inputs is None
+                mask_inputs = prev_sam_mask_logits
+            else:
+                assert mask_inputs is None
+            multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)
+
+            assert multimask_output
+            if point_inputs is not None:
+                point_inputs = {k: point_inputs[k].contiguous() for k in point_inputs}
+            sam_outputs = self._forward_sam_heads(
+                backbone_features=pix_feat.contiguous(),
+                point_inputs=point_inputs,
+                mask_inputs=mask_inputs,
+                high_res_features=[h.contiguous() for h in high_res_features],
+                multimask_output=multimask_output,
+            )
+
+        return current_out, sam_outputs, high_res_features, pix_feat
+
+    def _encode_memory_in_output(
+        self,
+        current_vision_feats,
+        feat_sizes,
+        point_inputs,
+        run_mem_encoder,
+        high_res_masks,
+        object_score_logits,
+        current_out,
+    ):
+        if run_mem_encoder and self.num_maskmem > 0:
+            high_res_masks_for_mem_enc = high_res_masks
+            maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+                current_vision_feats=current_vision_feats,
+                feat_sizes=feat_sizes,
+                pred_masks_high_res=high_res_masks_for_mem_enc,
+                object_score_logits=object_score_logits,
+                is_mask_from_pts=(point_inputs is not None),
+            )
+            current_out["maskmem_features"] = maskmem_features
+            current_out["maskmem_pos_enc"] = maskmem_pos_enc
+        else:
+            current_out["maskmem_features"] = None
+            current_out["maskmem_pos_enc"] = None
+
+    @torch.autograd.profiler.record_function("track_step")
+    def track_step(
+        self,
+        frame_idx,
+        is_init_cond_frame,
+        current_vision_feats,
+        current_vision_pos_embeds,
+        feat_sizes,
+        point_inputs,
+        mask_inputs,
+        output_dict,
+        num_frames,
+        track_in_reverse=False,  # tracking in reverse time order (for demo usage)
+        # Whether to run the memory encoder on the predicted masks. Sometimes we might want
+        # to skip the memory encoder with `run_mem_encoder=False`. For example,
+        # in demo we might call `track_step` multiple times for each user click,
+        # and only encode the memory when the user finalizes their clicks. And in ablation
+        # settings like SAM training on static images, we don't need the memory encoder.
+        run_mem_encoder=True,
+        # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).
+        prev_sam_mask_logits=None,
+    ):
+        current_out, sam_outputs, _, _ = self._track_step(
+            frame_idx,
+            is_init_cond_frame,
+            current_vision_feats,
+            current_vision_pos_embeds,
+            feat_sizes,
+            point_inputs,
+            mask_inputs,
+            output_dict,
+            num_frames,
+            track_in_reverse,
+            prev_sam_mask_logits,
+        )
+
+        (
+            _,
+            _,
+            _,
+            low_res_masks,
+            high_res_masks,
+            obj_ptr,
+            object_score_logits,
+        ) = sam_outputs
+
+        current_out["pred_masks"] = low_res_masks.clone()
+        current_out["pred_masks_high_res"] = high_res_masks.clone()
+        current_out["obj_ptr"] = obj_ptr.clone()
+        if not self.training:
+            # Only add this in inference (to avoid unused param in activation checkpointing;
+            # it's mainly used in the demo to encode spatial memories w/ consolidated masks)
+            current_out["object_score_logits"] = object_score_logits.clone()
+
+        # Finally run the memory encoder on the predicted mask to encode
+        # it into a new memory feature (that can be used in future frames)
+        self._encode_memory_in_output(
+            current_vision_feats,
+            feat_sizes,
+            point_inputs,
+            run_mem_encoder,
+            high_res_masks,
+            object_score_logits.clone(),
+            current_out,
+        )
+
+        return current_out
+
+    def _use_multimask(self, is_init_cond_frame, point_inputs):
+        """Whether to use multimask output in the SAM head."""
+        num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
+        multimask_output = (
+            self.multimask_output_in_sam
+            and (is_init_cond_frame or self.multimask_output_for_tracking)
+            and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)
+        )
+        return multimask_output
+
+    def _apply_non_overlapping_constraints(self, pred_masks):
+        """
+        Apply non-overlapping constraints to the object scores in pred_masks. Here we
+        keep only the highest scoring object at each spatial location in pred_masks.
+        """
+        batch_size = pred_masks.size(0)
+        if batch_size == 1:
+            return pred_masks
+
+        device = pred_masks.device
+        # "max_obj_inds": object index of the object with the highest score at each location
+        max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)
+        # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks`
+        batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]
+        keep = max_obj_inds == batch_obj_inds
+        # suppress overlapping regions' scores below -10.0 so that the foreground regions
+        # don't overlap (here sigmoid(-10.0)=4.5398e-05)
+        pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))
+        return pred_masks

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/modeling/sam2_utils.py

@@ -0,0 +1,323 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import copy
+from typing import Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torchao._models.sam2.utils.misc import mask_to_box
+
+
+def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):
+    """
+    Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`
+    that are temporally closest to the current frame at `frame_idx`. Here, we take
+    - a) the closest conditioning frame before `frame_idx` (if any);
+    - b) the closest conditioning frame after `frame_idx` (if any);
+    - c) any other temporally closest conditioning frames until reaching a total
+         of `max_cond_frame_num` conditioning frames.
+
+    Outputs:
+    - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.
+    - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.
+    """
+    if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:
+        selected_outputs = cond_frame_outputs
+        unselected_outputs = {}
+    else:
+        assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames"
+        selected_outputs = {}
+
+        # the closest conditioning frame before `frame_idx` (if any)
+        idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)
+        if idx_before is not None:
+            selected_outputs[idx_before] = cond_frame_outputs[idx_before]
+
+        # the closest conditioning frame after `frame_idx` (if any)
+        idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)
+        if idx_after is not None:
+            selected_outputs[idx_after] = cond_frame_outputs[idx_after]
+
+        # add other temporally closest conditioning frames until reaching a total
+        # of `max_cond_frame_num` conditioning frames.
+        num_remain = max_cond_frame_num - len(selected_outputs)
+        inds_remain = sorted(
+            (t for t in cond_frame_outputs if t not in selected_outputs),
+            key=lambda x: abs(x - frame_idx),
+        )[:num_remain]
+        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+        unselected_outputs = {
+            t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs
+        }
+
+    return selected_outputs, unselected_outputs
+
+
+def get_1d_sine_pe(pos_inds, dim, temperature=10000):
+    """
+    Get 1D sine positional embedding as in the original Transformer paper.
+    """
+    pe_dim = dim // 2
+    dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)
+    dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)
+
+    pos_embed = pos_inds.unsqueeze(-1) / dim_t
+    pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)
+    return pos_embed
+
+
+def get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+class DropPath(nn.Module):
+    # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
+    def __init__(self, drop_prob=0.0, scale_by_keep=True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        if self.drop_prob == 0.0 or not self.training:
+            return x
+        keep_prob = 1 - self.drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+        random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+        if keep_prob > 0.0 and self.scale_by_keep:
+            random_tensor.div_(keep_prob)
+        return x * random_tensor
+
+
+# Lightly adapted from
+# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
+class MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        num_layers: int,
+        activation: nn.Module = nn.ReLU,
+        sigmoid_output: bool = False,
+    ) -> None:
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+        self.sigmoid_output = sigmoid_output
+        self.act = activation()
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)
+        if self.sigmoid_output:
+            x = F.sigmoid(x)
+        return x
+
+
+# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
+# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
+class LayerNorm2d(nn.Module):
+    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(num_channels))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / torch.sqrt(s + self.eps)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+def sample_box_points(
+    masks: torch.Tensor,
+    noise: float = 0.1,  # SAM default
+    noise_bound: int = 20,  # SAM default
+    top_left_label: int = 2,
+    bottom_right_label: int = 3,
+) -> Tuple[np.array, np.array]:
+    """
+    Sample a noised version of the top left and bottom right corners of a given `bbox`
+
+    Inputs:
+    - masks: [B, 1, H,W] boxes, dtype=torch.Tensor
+    - noise: noise as a fraction of box width and height, dtype=float
+    - noise_bound: maximum amount of noise (in pure pixesl), dtype=int
+
+    Returns:
+    - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float
+    - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32
+    """
+    device = masks.device
+    box_coords = mask_to_box(masks)
+    B, _, H, W = masks.shape
+    box_labels = torch.tensor(
+        [top_left_label, bottom_right_label], dtype=torch.int, device=device
+    ).repeat(B)
+    if noise > 0.0:
+        if not isinstance(noise_bound, torch.Tensor):
+            noise_bound = torch.tensor(noise_bound, device=device)
+        bbox_w = box_coords[..., 2] - box_coords[..., 0]
+        bbox_h = box_coords[..., 3] - box_coords[..., 1]
+        max_dx = torch.min(bbox_w * noise, noise_bound)
+        max_dy = torch.min(bbox_h * noise, noise_bound)
+        box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1
+        box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1)
+
+        box_coords = box_coords + box_noise
+        img_bounds = (
+            torch.tensor([W, H, W, H], device=device) - 1
+        )  # uncentered pixel coords
+        box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds)  # In place clamping
+
+    box_coords = box_coords.reshape(-1, 2, 2)  # always 2 points
+    box_labels = box_labels.reshape(-1, 2)
+    return box_coords, box_labels
+
+
+def sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):
+    """
+    Sample `num_pt` random points (along with their labels) independently from the error regions.
+
+    Inputs:
+    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+    - num_pt: int, number of points to sample independently for each of the B error maps
+
+    Outputs:
+    - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+    - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means
+      negative clicks
+    """
+    if pred_masks is None:  # if pred_masks is not provided, treat it as empty
+        pred_masks = torch.zeros_like(gt_masks)
+    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+    assert num_pt >= 0
+
+    B, _, H_im, W_im = gt_masks.shape
+    device = gt_masks.device
+
+    # false positive region, a new point sampled in this region should have
+    # negative label to correct the FP error
+    fp_masks = ~gt_masks & pred_masks
+    # false negative region, a new point sampled in this region should have
+    # positive label to correct the FN error
+    fn_masks = gt_masks & ~pred_masks
+    # whether the prediction completely match the ground-truth on each mask
+    all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2)
+    all_correct = all_correct[..., None, None]
+
+    # channel 0 is FP map, while channel 1 is FN map
+    pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device)
+    # sample a negative new click from FP region or a positive new click
+    # from FN region, depend on where the maximum falls,
+    # and in case the predictions are all correct (no FP or FN), we just
+    # sample a negative click from the background region
+    pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks)
+    pts_noise[..., 1] *= fn_masks
+    pts_idx = pts_noise.flatten(2).argmax(dim=2)
+    labels = (pts_idx % 2).to(torch.int32)
+    pts_idx = pts_idx // 2
+    pts_x = pts_idx % W_im
+    pts_y = pts_idx // W_im
+    points = torch.stack([pts_x, pts_y], dim=2).to(torch.float)
+    return points, labels
+
+
+def sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):
+    """
+    Sample 1 random point (along with its label) from the center of each error region,
+    that is, the point with the largest distance to the boundary of each error region.
+    This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py
+
+    Inputs:
+    - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool
+    - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None
+    - padding: if True, pad with boundary of 1 px for distance transform
+
+    Outputs:
+    - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point
+    - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks
+    """
+    import cv2
+
+    if pred_masks is None:
+        pred_masks = torch.zeros_like(gt_masks)
+    assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1
+    assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape
+
+    B, _, _, W_im = gt_masks.shape
+    device = gt_masks.device
+
+    # false positive region, a new point sampled in this region should have
+    # negative label to correct the FP error
+    fp_masks = ~gt_masks & pred_masks
+    # false negative region, a new point sampled in this region should have
+    # positive label to correct the FN error
+    fn_masks = gt_masks & ~pred_masks
+
+    fp_masks = fp_masks.cpu().numpy()
+    fn_masks = fn_masks.cpu().numpy()
+    points = torch.zeros(B, 1, 2, dtype=torch.float)
+    labels = torch.ones(B, 1, dtype=torch.int32)
+    for b in range(B):
+        fn_mask = fn_masks[b, 0]
+        fp_mask = fp_masks[b, 0]
+        if padding:
+            fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), "constant")
+            fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), "constant")
+        # compute the distance of each point in FN/FP region to its boundary
+        fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)
+        fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)
+        if padding:
+            fn_mask_dt = fn_mask_dt[1:-1, 1:-1]
+            fp_mask_dt = fp_mask_dt[1:-1, 1:-1]
+
+        # take the point in FN/FP region with the largest distance to its boundary
+        fn_mask_dt_flat = fn_mask_dt.reshape(-1)
+        fp_mask_dt_flat = fp_mask_dt.reshape(-1)
+        fn_argmax = np.argmax(fn_mask_dt_flat)
+        fp_argmax = np.argmax(fp_mask_dt_flat)
+        is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax]
+        pt_idx = fn_argmax if is_positive else fp_argmax
+        points[b, 0, 0] = pt_idx % W_im  # x
+        points[b, 0, 1] = pt_idx // W_im  # y
+        labels[b, 0] = int(is_positive)
+
+    points = points.to(device)
+    labels = labels.to(device)
+    return points, labels
+
+
+def get_next_point(gt_masks, pred_masks, method):
+    if method == "uniform":
+        return sample_random_points_from_errors(gt_masks, pred_masks)
+    elif method == "center":
+        return sample_one_point_from_error_center(gt_masks, pred_masks)
+    else:
+        raise ValueError(f"unknown sampling method {method}")

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_hiera_b+.yaml

@@ -0,0 +1,113 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 112
+      num_heads: 2
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [896, 448, 224, 112]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_hiera_l.yaml

@@ -0,0 +1,117 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 144
+      num_heads: 2
+      stages: [2, 6, 36, 4]
+      global_att_blocks: [23, 33, 43]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+      window_spec: [8, 4, 16, 8]
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [1152, 576, 288, 144]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_hiera_s.yaml

@@ -0,0 +1,116 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 96
+      num_heads: 1
+      stages: [1, 2, 11, 2]
+      global_att_blocks: [7, 10, 13]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [768, 384, 192, 96]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_hiera_t.yaml

@@ -0,0 +1,118 @@
+# @package _global_
+
+# Model
+model:
+  _target_: torchao._models.sam2.modeling.sam2_base.SAM2Base
+  image_encoder:
+    _target_: torchao._models.sam2.modeling.backbones.image_encoder.ImageEncoder
+    scalp: 1
+    trunk:
+      _target_: torchao._models.sam2.modeling.backbones.hieradet.Hiera
+      embed_dim: 96
+      num_heads: 1
+      stages: [1, 2, 7, 2]
+      global_att_blocks: [5, 7, 9]
+      window_pos_embed_bkg_spatial_size: [7, 7]
+    neck:
+      _target_: torchao._models.sam2.modeling.backbones.image_encoder.FpnNeck
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 256
+        normalize: true
+        scale: null
+        temperature: 10000
+      d_model: 256
+      backbone_channel_list: [768, 384, 192, 96]
+      fpn_top_down_levels: [2, 3]  # output level 0 and 1 directly use the backbone features
+      fpn_interp_model: nearest
+
+  memory_attention:
+    _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttention
+    d_model: 256
+    pos_enc_at_input: true
+    layer:
+      _target_: torchao._models.sam2.modeling.memory_attention.MemoryAttentionLayer
+      activation: relu
+      dim_feedforward: 2048
+      dropout: 0.1
+      pos_enc_at_attn: false
+      self_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+      d_model: 256
+      pos_enc_at_cross_attn_keys: true
+      pos_enc_at_cross_attn_queries: false
+      cross_attention:
+        _target_: torchao._models.sam2.modeling.sam.transformer.RoPEAttention
+        rope_theta: 10000.0
+        feat_sizes: [32, 32]
+        rope_k_repeat: True
+        embedding_dim: 256
+        num_heads: 1
+        downsample_rate: 1
+        dropout: 0.1
+        kv_in_dim: 64
+    num_layers: 4
+
+  memory_encoder:
+      _target_: torchao._models.sam2.modeling.memory_encoder.MemoryEncoder
+      out_dim: 64
+      position_encoding:
+        _target_: torchao._models.sam2.modeling.position_encoding.PositionEmbeddingSine
+        num_pos_feats: 64
+        normalize: true
+        scale: null
+        temperature: 10000
+      mask_downsampler:
+        _target_: torchao._models.sam2.modeling.memory_encoder.MaskDownSampler
+        kernel_size: 3
+        stride: 2
+        padding: 1
+      fuser:
+        _target_: torchao._models.sam2.modeling.memory_encoder.Fuser
+        layer:
+          _target_: torchao._models.sam2.modeling.memory_encoder.CXBlock
+          dim: 256
+          kernel_size: 7
+          padding: 3
+          layer_scale_init_value: 1e-6
+          use_dwconv: True  # depth-wise convs
+        num_layers: 2
+
+  num_maskmem: 7
+  image_size: 1024
+  # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
+  # SAM decoder
+  sigmoid_scale_for_mem_enc: 20.0
+  sigmoid_bias_for_mem_enc: -10.0
+  use_mask_input_as_output_without_sam: true
+  # Memory
+  directly_add_no_mem_embed: true
+  # use high-resolution feature map in the SAM mask decoder
+  use_high_res_features_in_sam: true
+  # output 3 masks on the first click on initial conditioning frames
+  multimask_output_in_sam: true
+  # SAM heads
+  iou_prediction_use_sigmoid: True
+  # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
+  use_obj_ptrs_in_encoder: true
+  add_tpos_enc_to_obj_ptrs: false
+  only_obj_ptrs_in_the_past_for_eval: true
+  # object occlusion prediction
+  pred_obj_scores: true
+  pred_obj_scores_mlp: true
+  fixed_no_obj_ptr: true
+  # multimask tracking settings
+  multimask_output_for_tracking: true
+  use_multimask_token_for_obj_ptr: true
+  multimask_min_pt_num: 0
+  multimask_max_pt_num: 1
+  use_mlp_for_obj_ptr_proj: true
+  # Compilation flag
+  # HieraT does not currently support compilation, should always be set to False
+  compile_image_encoder: False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_image_predictor.py

@@ -0,0 +1,571 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from PIL.Image import Image
+
+from torchao._models.sam2.modeling.sam2_base import SAM2Base
+from torchao._models.sam2.utils.misc import get_image_size
+from torchao._models.sam2.utils.transforms import SAM2Transforms
+
+
+class SAM2ImagePredictor(torch.nn.Module):
+    def __init__(
+        self,
+        sam_model: SAM2Base,
+        mask_threshold=0.0,
+        max_hole_area=0.0,
+        max_sprinkle_area=0.0,
+        **kwargs,
+    ) -> None:
+        """
+        Uses SAM-2 to calculate the image embedding for an image, and then
+        allow repeated, efficient mask prediction given prompts.
+
+        Arguments:
+          sam_model (Sam-2): The model to use for mask prediction.
+          mask_threshold (float): The threshold to use when converting mask logits
+            to binary masks. Masks are thresholded at 0 by default.
+          max_hole_area (int): If max_hole_area > 0, we fill small holes in up to
+            the maximum area of max_hole_area in low_res_masks.
+          max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to
+            the maximum area of max_sprinkle_area in low_res_masks.
+        """
+        super().__init__()
+        self.model = sam_model
+        self._transforms = SAM2Transforms(
+            resolution=self.model.image_size,
+            mask_threshold=mask_threshold,
+            max_hole_area=max_hole_area,
+            max_sprinkle_area=max_sprinkle_area,
+        )
+
+        # Predictor state
+        self._is_image_set = False
+        self._features = None
+        self._orig_hw = None
+        # Whether the predictor is set for single image or a batch of images
+        self._is_batch = False
+
+        # Predictor config
+        self.mask_threshold = mask_threshold
+
+        # Spatial dim for backbone feature maps
+        self._bb_feat_sizes = [
+            (256, 256),
+            (128, 128),
+            (64, 64),
+        ]
+
+        self._image_dtype = torch.float32
+        self._transforms_device = "cpu"
+
+    @classmethod
+    def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2ImagePredictor":
+        """
+        Load a pretrained model from the Hugging Face hub.
+
+        Arguments:
+          model_id (str): The Hugging Face repository ID.
+          **kwargs: Additional arguments to pass to the model constructor.
+
+        Returns:
+          (SAM2ImagePredictor): The loaded model.
+        """
+        from sam2.build_sam import build_sam2_hf
+
+        sam_model = build_sam2_hf(model_id, **kwargs)
+        return cls(sam_model, **kwargs)
+
+    @torch.no_grad()
+    def set_image(
+        self,
+        image: Union[np.ndarray, Image, torch.Tensor],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image, allowing
+        masks to be predicted with the 'predict' method.
+
+        Arguments:
+          image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image
+          with pixel values in [0, 255].
+          image_format (str): The color format of the image, in ['RGB', 'BGR'].
+        """
+        self.reset_predictor()
+        # Transform the image to the form expected by the model
+        self._orig_hw = [get_image_size(image)]
+
+        if isinstance(image, torch.Tensor):
+            # from torchvision.transforms.v2 import functional as F
+            # input_image = F.to_dtype(image, torch.float32, scale=True)
+            input_image = image
+        else:
+            input_image = self._transforms.to_tensor(image)
+
+        # 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)
+        # input_image = self._transforms.transforms(input_image)
+        input_image = input_image[None, ...].to(dtype=self._image_dtype)
+
+        assert len(input_image.shape) == 4 and input_image.shape[1] == 3, (
+            f"input_image must be of size 1x3xHxW, got {input_image.shape}"
+        )
+        logging.info("Computing image embeddings for the provided image...")
+        with torch.autograd.profiler.record_function("forward_image"):
+            backbone_out = self.model.forward_image(input_image)
+        _, 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
+
+        feats = [
+            feat.permute(1, 2, 0).view(1, -1, *feat_size)
+            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+        ][::-1]
+        self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+        self._is_image_set = True
+        logging.info("Image embeddings computed.")
+
+    @torch.no_grad()
+    def set_image_batch(
+        self,
+        image_list: List[Union[np.ndarray, torch.Tensor]],
+    ) -> None:
+        """
+        Calculates the image embeddings for the provided image batch, allowing
+        masks to be predicted with the 'predict_batch' method.
+
+        Arguments:
+          image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray
+          with pixel values in [0, 255].
+        """
+        self.reset_predictor()
+        assert isinstance(image_list, list)
+        self._orig_hw = list(map(get_image_size, image_list))
+        with torch.autograd.profiler.record_function("forward_batch"):
+            # Transform the image to the form expected by the model
+            # img_batch = self._transforms.forward_batch(image_list)
+            image_list = [
+                self._transforms.to_tensor(img) if isinstance(img, np.ndarray) else img
+                for img in image_list
+            ]
+            image_list = [self._transforms.transforms(img) for img in image_list]
+            img_batch = torch.stack(image_list, dim=0)
+            img_batch = img_batch.to(self.device)
+            img_batch = img_batch.to(self._image_dtype)
+        batch_size = img_batch.shape[0]
+        assert 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...")
+        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
+
+        feats = [
+            feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)
+            for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])
+        ][::-1]
+        self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]}
+        self._is_image_set = True
+        self._is_batch = True
+        logging.info("Image embeddings computed.")
+
+    def predict_batch(
+        self,
+        point_coords_batch: List[np.ndarray] = None,
+        point_labels_batch: List[np.ndarray] = None,
+        box_batch: List[np.ndarray] = None,
+        mask_input_batch: List[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        normalize_coords=True,
+        return_type: str = "numpy",
+    ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+        """This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.
+        It returns a tuple of lists of masks, ious, and low_res_masks_logits.
+        """
+        if return_type not in ["numpy", "torch"]:
+            raise ValueError(
+                f"Expected return_type to be either numpy or torch, but got {return_type}"
+            )
+        assert self._is_batch, "This function should only be used when in batched mode"
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image_batch(...) before mask prediction."
+            )
+        num_images = len(self._features["image_embed"])
+        all_masks = []
+        all_ious = []
+        all_low_res_masks = []
+        for img_idx in range(num_images):
+            # Transform input prompts
+            point_coords = (
+                point_coords_batch[img_idx] if point_coords_batch is not None else None
+            )
+            point_labels = (
+                point_labels_batch[img_idx] if point_labels_batch is not None else None
+            )
+            box = box_batch[img_idx] if box_batch is not None else None
+            mask_input = (
+                mask_input_batch[img_idx] if mask_input_batch is not None else None
+            )
+            mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+                point_coords,
+                point_labels,
+                box,
+                mask_input,
+                normalize_coords,
+                img_idx=img_idx,
+            )
+            masks, iou_predictions, low_res_masks = self._predict(
+                unnorm_coords,
+                labels,
+                unnorm_box,
+                mask_input,
+                multimask_output,
+                return_logits=return_logits,
+                img_idx=img_idx,
+            )
+            if return_type == "numpy":
+                masks = masks.squeeze(0).float().detach().cpu().numpy()
+                iou_predictions = (
+                    iou_predictions.squeeze(0).float().detach().cpu().numpy()
+                )
+                low_res_masks = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+            # NOTE: Need these additional clones to prevent overwriting tensor output of CUDAGraph
+            all_masks.append(masks.clone())
+            all_ious.append(iou_predictions.clone())
+            all_low_res_masks.append(low_res_masks.clone())
+
+        return all_masks, all_ious, all_low_res_masks
+
+    def predict(
+        self,
+        point_coords: Optional[np.ndarray] = None,
+        point_labels: Optional[np.ndarray] = None,
+        box: Optional[np.ndarray] = None,
+        mask_input: Optional[np.ndarray] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        normalize_coords=True,
+        return_type: str = "numpy",
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+
+        Arguments:
+          point_coords (np.ndarray or None): A Nx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (np.ndarray or None): A length N array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          box (np.ndarray or None): A length 4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form 1xHxW, where
+            for SAM, H=W=256.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+          normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.
+
+        Returns:
+          (np.ndarray): The output masks in CxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (np.ndarray): An array of length C containing the model's
+            predictions for the quality of each mask.
+          (np.ndarray): An array of shape CxHxW, where C is the number
+            of masks and H=W=256. These low resolution logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if return_type not in ["numpy", "torch"]:
+            raise ValueError(
+                f"Expected return_type to be either numpy or torch, but got {return_type}"
+            )
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        # Transform input prompts
+
+        mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(
+            point_coords, point_labels, box, mask_input, normalize_coords
+        )
+
+        masks, iou_predictions, low_res_masks = self._predict(
+            unnorm_coords,
+            labels,
+            unnorm_box,
+            mask_input,
+            multimask_output,
+            return_logits=return_logits,
+        )
+
+        if return_type == "torch":
+            return (
+                masks.squeeze(0),
+                iou_predictions.squeeze(0),
+                low_res_masks.squeeze(0),
+            )
+
+        masks_np = masks.squeeze(0).float().detach().cpu().numpy()
+        iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()
+        low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()
+        return masks_np, iou_predictions_np, low_res_masks_np
+
+    def _prep_prompts(
+        self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1
+    ):
+        unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None
+        if point_coords is not None:
+            assert point_labels is not None, (
+                "point_labels must be supplied if point_coords is supplied."
+            )
+            point_coords = (
+                torch.as_tensor(point_coords, dtype=torch.float)
+                .pin_memory()
+                .to(self.device, non_blocking=True)
+            )
+            unnorm_coords = self._transforms.transform_coords(
+                point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+            )
+            labels = (
+                torch.as_tensor(point_labels, dtype=torch.int)
+                .pin_memory()
+                .to(self.device, non_blocking=True)
+            )
+            if len(unnorm_coords.shape) == 2:
+                unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]
+        if box is not None:
+            box = torch.as_tensor(box, dtype=torch.float, device=self.device)
+            unnorm_box = self._transforms.transform_boxes(
+                box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]
+            )  # Bx2x2
+        if mask_logits is not None:
+            mask_input = torch.as_tensor(
+                mask_logits, dtype=torch.float, device=self.device
+            )
+            if len(mask_input.shape) == 3:
+                mask_input = mask_input[None, :, :, :]
+        return mask_input, unnorm_coords, labels, unnorm_box
+
+    @torch.no_grad()
+    def _predict(
+        self,
+        point_coords: Optional[torch.Tensor],
+        point_labels: Optional[torch.Tensor],
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+        return_logits: bool = False,
+        img_idx: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Predict masks for the given input prompts, using the currently set image.
+        Input prompts are batched torch tensors and are expected to already be
+        transformed to the input frame using SAM2Transforms.
+
+        Arguments:
+          point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
+            model. Each point is in (X,Y) in pixels.
+          point_labels (torch.Tensor or None): A BxN array of labels for the
+            point prompts. 1 indicates a foreground point and 0 indicates a
+            background point.
+          boxes (np.ndarray or None): A Bx4 array given a box prompt to the
+            model, in XYXY format.
+          mask_input (np.ndarray): A low resolution mask input to the model, typically
+            coming from a previous prediction iteration. Has form Bx1xHxW, where
+            for SAM, H=W=256. Masks returned by a previous iteration of the
+            predict method do not need further transformation.
+          multimask_output (bool): If true, the model will return three masks.
+            For ambiguous input prompts (such as a single click), this will often
+            produce better masks than a single prediction. If only a single
+            mask is needed, the model's predicted quality score can be used
+            to select the best mask. For non-ambiguous prompts, such as multiple
+            input prompts, multimask_output=False can give better results.
+          return_logits (bool): If true, returns un-thresholded masks logits
+            instead of a binary mask.
+
+        Returns:
+          (torch.Tensor): The output masks in BxCxHxW format, where C is the
+            number of masks, and (H, W) is the original image size.
+          (torch.Tensor): An array of shape BxC containing the model's
+            predictions for the quality of each mask.
+          (torch.Tensor): An array of shape BxCxHxW, where C is the number
+            of masks and H=W=256. These low res logits can be passed to
+            a subsequent iteration as mask input.
+        """
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) before mask prediction."
+            )
+
+        with torch.autograd.profiler.record_function("_predict_masks"):
+            high_res_feats = self._features["high_res_feats"]
+            image_embed = self._features["image_embed"]
+            image_pe = self.model.sam_prompt_encoder.get_dense_pe().clone()
+            high_res_feats_input = [
+                feat_level[img_idx].unsqueeze(0).clone()
+                # for feat_level in self._features["high_res_feats"]
+                for feat_level in high_res_feats
+            ]
+            image_embed_input = image_embed[img_idx].unsqueeze(0).clone()
+            assert boxes is None
+            assert mask_input is None
+            assert multimask_output is True
+            low_res_masks, iou_predictions = self._predict_masks(
+                [t.contiguous() for t in high_res_feats_input],
+                image_embed_input.contiguous(),
+                image_pe.contiguous(),
+                point_coords.contiguous(),
+                point_labels.contiguous(),
+                boxes=boxes,
+                mask_input=mask_input,
+                multimask_output=multimask_output,
+            )
+            # img_idx=img_idx)
+        with torch.autograd.profiler.record_function("_predict_masks_postprocess"):
+            masks, low_res_masks = self._predict_masks_postprocess(
+                low_res_masks, img_idx, return_logits
+            )
+            return masks, iou_predictions, low_res_masks
+
+    def _predict_masks(
+        self,
+        high_res_feats_input,
+        image_embed,
+        image_pe,
+        point_coords,
+        point_labels,
+        boxes: Optional[torch.Tensor] = None,
+        mask_input: Optional[torch.Tensor] = None,
+        multimask_output: bool = True,
+    ):
+        # NOTE: img_idx causes unnecessary recompilations, because
+        # the int guard will fail otherwise.
+        #   img_idx: int = -1):
+        if point_coords is not None:
+            concat_points = (point_coords, point_labels)
+        else:
+            concat_points = None
+
+        # Embed prompts
+        if boxes is not None:
+            box_coords = boxes.reshape(-1, 2, 2)
+            box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)
+            box_labels = box_labels.repeat(boxes.size(0), 1)
+            # we merge "boxes" and "points" into a single "concat_points" input (where
+            # boxes are added at the beginning) to sam_prompt_encoder
+            if concat_points is not None:
+                concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)
+                concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)
+                concat_points = (concat_coords, concat_labels)
+            else:
+                concat_points = (box_coords, box_labels)
+
+        with torch.autograd.profiler.record_function("self.model.sam_prompt_encoder"):
+            sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
+                points=concat_points,
+                boxes=None,
+                masks=mask_input,
+            )
+
+        # Predict masks
+        batched_mode = (
+            concat_points is not None and concat_points[0].shape[0] > 1
+        )  # multi object prediction
+        # high_res_features = [
+        #     feat_level[img_idx].unsqueeze(0).clone()
+        #     # for feat_level in self._features["high_res_feats"]
+        #     for feat_level in high_res_feats_input
+        # ]
+        high_res_features = high_res_feats_input
+        with torch.autograd.profiler.record_function("self.model.sam_mask_decoder"):
+            # if not multimask_output:
+            #     raise ValueError("Expected multimask_output.")
+            # if batched_mode:
+            #     raise ValueError("Did not expected repeat_image.")
+            low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(
+                # image_embeddings=self._features["image_embed"][img_idx].unsqueeze(0).clone(),
+                # image_embeddings=image_embed[img_idx].unsqueeze(0).clone(),
+                image_embeddings=image_embed,
+                # image_pe=self.model.sam_prompt_encoder.get_dense_pe().clone(),
+                image_pe=image_pe,
+                sparse_prompt_embeddings=sparse_embeddings.clone(),
+                dense_prompt_embeddings=dense_embeddings.clone(),
+                multimask_output=multimask_output,
+                repeat_image=batched_mode,
+                high_res_features=high_res_features,
+            )
+
+        return low_res_masks, iou_predictions
+
+    def _predict_masks_postprocess(
+        self, low_res_masks, img_idx, return_logits, channel_1=False
+    ):
+        # TODO: Might want to defer this until after data["iou_preds"] > self.pred_iou_thresh
+        with torch.autograd.profiler.record_function(
+            "self._transforms.postprocess_masks"
+        ):
+            # 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], self._image_dtype
+                )
+            else:
+                masks = self._transforms.postprocess_masks(
+                    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:
+            masks = masks > self.mask_threshold
+
+        return masks, low_res_masks
+
+    def get_image_embedding(self) -> torch.Tensor:
+        """
+        Returns the image embeddings for the currently set image, with
+        shape 1xCxHxW, where C is the embedding dimension and (H,W) are
+        the embedding spatial dimension of SAM (typically C=256, H=W=64).
+        """
+        if not self._is_image_set:
+            raise RuntimeError(
+                "An image must be set with .set_image(...) to generate an embedding."
+            )
+        assert self._features is not None, (
+            "Features must exist if an image has been set."
+        )
+        return self._features["image_embed"]
+
+    @property
+    def device(self) -> torch.device:
+        return self.model.device
+
+    def reset_predictor(self) -> None:
+        """
+        Resets the image embeddings and other state variables.
+        """
+        self._is_image_set = False
+        self._features = None
+        self._orig_hw = None
+        self._is_batch = False

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/sam2_video_predictor.py

@@ -0,0 +1,1192 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import warnings
+from collections import OrderedDict
+
+import torch
+from tqdm import tqdm
+
+from torchao._models.sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
+from torchao._models.sam2.utils.misc import (
+    concat_points,
+    fill_holes_in_mask_scores,
+    load_video_frames,
+)
+
+
+class SAM2VideoPredictor(SAM2Base):
+    """The predictor class to handle user interactions and manage inference states."""
+
+    def __init__(
+        self,
+        fill_hole_area=0,
+        # whether to apply non-overlapping constraints on the output object masks
+        non_overlap_masks=False,
+        # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;
+        # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)
+        clear_non_cond_mem_around_input=False,
+        # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).
+        clear_non_cond_mem_for_multi_obj=False,
+        # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click
+        # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames
+        add_all_frames_to_correct_as_cond=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.fill_hole_area = fill_hole_area
+        self.non_overlap_masks = non_overlap_masks
+        self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input
+        self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj
+        self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond
+
+    @staticmethod
+    def batch_inference_states(inference_states: list):
+        assert all(dict == type(state) for state in inference_states)
+        num_states = len(inference_states)
+        assert num_states > 0
+        import copy
+
+        batched_inference_state = copy.copy(inference_states[0])
+
+        from torchao._models.sam2.map_tensor import to_map_tensor
+
+        # NOTE: Making a build assumption only images differ
+        all_images = torch.stack([state["images"] for state in inference_states])
+        batched_inference_state["images"] = to_map_tensor(all_images)
+        return batched_inference_state
+
+    @torch.no_grad()
+    def init_state(
+        self,
+        video_path,
+        offload_video_to_cpu=False,
+        offload_state_to_cpu=False,
+        async_loading_frames=False,
+    ):
+        """Initialize an inference state."""
+        compute_device = self.device  # device of the model
+        images, video_height, video_width = load_video_frames(
+            video_path=video_path,
+            image_size=self.image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            async_loading_frames=async_loading_frames,
+            compute_device=compute_device,
+        )
+        inference_state = {}
+        inference_state["images"] = images
+        inference_state["num_frames"] = len(images)
+        # whether to offload the video frames to CPU memory
+        # turning on this option saves the GPU memory with only a very small overhead
+        inference_state["offload_video_to_cpu"] = offload_video_to_cpu
+        # whether to offload the inference state to CPU memory
+        # turning on this option saves the GPU memory at the cost of a lower tracking fps
+        # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object
+        # and from 24 to 21 when tracking two objects)
+        inference_state["offload_state_to_cpu"] = offload_state_to_cpu
+        # the original video height and width, used for resizing final output scores
+        inference_state["video_height"] = video_height
+        inference_state["video_width"] = video_width
+        inference_state["device"] = compute_device
+        if offload_state_to_cpu:
+            inference_state["storage_device"] = torch.device("cpu")
+        else:
+            inference_state["storage_device"] = compute_device
+        # inputs on each frame
+        inference_state["point_inputs_per_obj"] = {}
+        inference_state["mask_inputs_per_obj"] = {}
+        # visual features on a small number of recently visited frames for quick interactions
+        inference_state["cached_features"] = {}
+        # values that don't change across frames (so we only need to hold one copy of them)
+        inference_state["constants"] = {}
+        # mapping between client-side object id and model-side object index
+        inference_state["obj_id_to_idx"] = OrderedDict()
+        inference_state["obj_idx_to_id"] = OrderedDict()
+        inference_state["obj_ids"] = []
+        # A storage to hold the model's tracking results and states on each frame
+        inference_state["output_dict"] = {
+            "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+        }
+        # Slice (view) of each object tracking results, sharing the same memory with "output_dict"
+        inference_state["output_dict_per_obj"] = {}
+        # A temporary storage to hold new outputs when user interact with a frame
+        # to add clicks or mask (it's merged into "output_dict" before propagation starts)
+        inference_state["temp_output_dict_per_obj"] = {}
+        # Frames that already holds consolidated outputs from click or mask inputs
+        # (we directly use their consolidated outputs during tracking)
+        inference_state["consolidated_frame_inds"] = {
+            "cond_frame_outputs": set(),  # set containing frame indices
+            "non_cond_frame_outputs": set(),  # set containing frame indices
+        }
+        # metadata for each tracking frame (e.g. which direction it's tracked)
+        inference_state["tracking_has_started"] = False
+        inference_state["frames_already_tracked"] = {}
+        # Warm up the visual backbone and cache the image feature on frame 0
+        self._get_image_feature(inference_state, frame_idx=0, batch_size=1)
+        return inference_state
+
+    @classmethod
+    def from_pretrained(cls, model_id: str, **kwargs) -> "SAM2VideoPredictor":
+        """
+        Load a pretrained model from the Hugging Face hub.
+
+        Arguments:
+          model_id (str): The Hugging Face repository ID.
+          **kwargs: Additional arguments to pass to the model constructor.
+
+        Returns:
+          (SAM2VideoPredictor): The loaded model.
+        """
+        from sam2.build_sam import build_sam2_video_predictor_hf
+
+        sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)
+        return sam_model
+
+    def _obj_id_to_idx(self, inference_state, obj_id):
+        """Map client-side object id to model-side object index."""
+        obj_idx = inference_state["obj_id_to_idx"].get(obj_id, None)
+        if obj_idx is not None:
+            return obj_idx
+
+        # This is a new object id not sent to the server before. We only allow adding
+        # new objects *before* the tracking starts.
+        allow_new_object = not inference_state["tracking_has_started"]
+        if allow_new_object:
+            # get the next object slot
+            obj_idx = len(inference_state["obj_id_to_idx"])
+            inference_state["obj_id_to_idx"][obj_id] = obj_idx
+            inference_state["obj_idx_to_id"][obj_idx] = obj_id
+            inference_state["obj_ids"] = list(inference_state["obj_id_to_idx"])
+            # set up input and output structures for this object
+            inference_state["point_inputs_per_obj"][obj_idx] = {}
+            inference_state["mask_inputs_per_obj"][obj_idx] = {}
+            inference_state["output_dict_per_obj"][obj_idx] = {
+                "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+                "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            }
+            inference_state["temp_output_dict_per_obj"][obj_idx] = {
+                "cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+                "non_cond_frame_outputs": {},  # dict containing {frame_idx: <out>}
+            }
+            return obj_idx
+        else:
+            raise RuntimeError(
+                f"Cannot add new object id {obj_id} after tracking starts. "
+                f"All existing object ids: {inference_state['obj_ids']}. "
+                f"Please call 'reset_state' to restart from scratch."
+            )
+
+    def _obj_idx_to_id(self, inference_state, obj_idx):
+        """Map model-side object index to client-side object id."""
+        return inference_state["obj_idx_to_id"][obj_idx]
+
+    def _get_obj_num(self, inference_state):
+        """Get the total number of unique object ids received so far in this session."""
+        return len(inference_state["obj_idx_to_id"])
+
+    @torch.no_grad()
+    def add_new_points_or_box(
+        self,
+        inference_state,
+        frame_idx,
+        obj_id,
+        points=None,
+        labels=None,
+        clear_old_points=True,
+        normalize_coords=True,
+        box=None,
+    ):
+        """Add new points to a frame."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+        point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+        mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+        if (points is not None) != (labels is not None):
+            raise ValueError("points and labels must be provided together")
+        if points is None and box is None:
+            raise ValueError("at least one of points or box must be provided as input")
+
+        if points is None:
+            points = torch.zeros(0, 2, dtype=torch.float32)
+        elif not isinstance(points, torch.Tensor):
+            points = torch.tensor(points, dtype=torch.float32)
+        if labels is None:
+            labels = torch.zeros(0, dtype=torch.int32)
+        elif not isinstance(labels, torch.Tensor):
+            labels = torch.tensor(labels, dtype=torch.int32)
+        if points.dim() == 2:
+            points = points.unsqueeze(0)  # add batch dimension
+        if labels.dim() == 1:
+            labels = labels.unsqueeze(0)  # add batch dimension
+
+        # If `box` is provided, we add it as the first two points with labels 2 and 3
+        # along with the user-provided points (consistent with how SAM 2 is trained).
+        if box is not None:
+            if not clear_old_points:
+                raise ValueError(
+                    "cannot add box without clearing old points, since "
+                    "box prompt must be provided before any point prompt "
+                    "(please use clear_old_points=True instead)"
+                )
+            if inference_state["tracking_has_started"]:
+                warnings.warn(
+                    "You are adding a box after tracking starts. SAM 2 may not always be "
+                    "able to incorporate a box prompt for *refinement*. If you intend to "
+                    "use box prompt as an *initial* input before tracking, please call "
+                    "'reset_state' on the inference state to restart from scratch.",
+                    category=UserWarning,
+                    stacklevel=2,
+                )
+            if not isinstance(box, torch.Tensor):
+                box = torch.tensor(box, dtype=torch.float32, device=points.device)
+            box_coords = box.reshape(1, 2, 2)
+            box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)
+            box_labels = box_labels.reshape(1, 2)
+            points = torch.cat([box_coords, points], dim=1)
+            labels = torch.cat([box_labels, labels], dim=1)
+
+        if normalize_coords:
+            video_H = inference_state["video_height"]
+            video_W = inference_state["video_width"]
+            points = points / torch.tensor([video_W, video_H]).to(points.device)
+        # scale the (normalized) coordinates by the model's internal image size
+        points = points * self.image_size
+        points = points.to(inference_state["device"])
+        labels = labels.to(inference_state["device"])
+
+        if not clear_old_points:
+            point_inputs = point_inputs_per_frame.get(frame_idx, None)
+        else:
+            point_inputs = None
+        point_inputs = concat_points(point_inputs, points, labels)
+
+        point_inputs_per_frame[frame_idx] = point_inputs
+        mask_inputs_per_frame.pop(frame_idx, None)
+        # If this frame hasn't been tracked before, we treat it as an initial conditioning
+        # frame, meaning that the inputs points are to generate segments on this frame without
+        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+        # the input points will be used to correct the already tracked masks.
+        is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+        # whether to track in reverse time order
+        if is_init_cond_frame:
+            reverse = False
+        else:
+            reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+        obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+        obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+        # Add a frame to conditioning output if it's an initial conditioning frame or
+        # if the model sees all frames receiving clicks/mask as conditioning frames.
+        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+        # Get any previously predicted mask logits on this object and feed it along with
+        # the new clicks into the SAM mask decoder.
+        prev_sam_mask_logits = None
+        # lookup temporary output dict first, which contains the most recent output
+        # (if not found, then lookup conditioning and non-conditioning frame output)
+        prev_out = obj_temp_output_dict[storage_key].get(frame_idx)
+        if prev_out is None:
+            prev_out = obj_output_dict["cond_frame_outputs"].get(frame_idx)
+            if prev_out is None:
+                prev_out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx)
+
+        if prev_out is not None and prev_out["pred_masks"] is not None:
+            device = inference_state["device"]
+            prev_sam_mask_logits = prev_out["pred_masks"].to(device, non_blocking=True)
+            # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.
+            prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)
+        current_out, _ = self._run_single_frame_inference(
+            inference_state=inference_state,
+            output_dict=obj_output_dict,  # run on the slice of a single object
+            frame_idx=frame_idx,
+            batch_size=1,  # run on the slice of a single object
+            is_init_cond_frame=is_init_cond_frame,
+            point_inputs=point_inputs,
+            mask_inputs=None,
+            reverse=reverse,
+            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+            # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+            # allows us to enforce non-overlapping constraints on all objects before encoding
+            # them into memory.
+            run_mem_encoder=False,
+            prev_sam_mask_logits=prev_sam_mask_logits,
+        )
+        # Add the output to the output dict (to be used as future memory)
+        obj_temp_output_dict[storage_key][frame_idx] = current_out
+
+        # Resize the output mask to the original video resolution
+        obj_ids = inference_state["obj_ids"]
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        return frame_idx, obj_ids, video_res_masks
+
+    def add_new_points(self, *args, **kwargs):
+        """Deprecated method. Please use `add_new_points_or_box` instead."""
+        return self.add_new_points_or_box(*args, **kwargs)
+
+    @torch.no_grad()
+    def add_new_mask(
+        self,
+        inference_state,
+        frame_idx,
+        obj_id,
+        mask,
+    ):
+        """Add new mask to a frame."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+        point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx]
+        mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx]
+
+        if not isinstance(mask, torch.Tensor):
+            mask = torch.tensor(mask, dtype=torch.bool)
+        assert mask.dim() == 2
+        mask_H, mask_W = mask.shape
+        mask_inputs_orig = mask[None, None]  # add batch and channel dimension
+        mask_inputs_orig = mask_inputs_orig.float().to(inference_state["device"])
+
+        # resize the mask if it doesn't match the model's image size
+        if mask_H != self.image_size or mask_W != self.image_size:
+            mask_inputs = torch.nn.functional.interpolate(
+                mask_inputs_orig,
+                size=(self.image_size, self.image_size),
+                align_corners=False,
+                mode="bilinear",
+                antialias=True,  # use antialias for downsampling
+            )
+            mask_inputs = (mask_inputs >= 0.5).float()
+        else:
+            mask_inputs = mask_inputs_orig
+
+        mask_inputs_per_frame[frame_idx] = mask_inputs
+        point_inputs_per_frame.pop(frame_idx, None)
+        # If this frame hasn't been tracked before, we treat it as an initial conditioning
+        # frame, meaning that the inputs points are to generate segments on this frame without
+        # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),
+        # the input points will be used to correct the already tracked masks.
+        is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"]
+        # whether to track in reverse time order
+        if is_init_cond_frame:
+            reverse = False
+        else:
+            reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"]
+        obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+        obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+        # Add a frame to conditioning output if it's an initial conditioning frame or
+        # if the model sees all frames receiving clicks/mask as conditioning frames.
+        is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+
+        current_out, _ = self._run_single_frame_inference(
+            inference_state=inference_state,
+            output_dict=obj_output_dict,  # run on the slice of a single object
+            frame_idx=frame_idx,
+            batch_size=1,  # run on the slice of a single object
+            is_init_cond_frame=is_init_cond_frame,
+            point_inputs=None,
+            mask_inputs=mask_inputs,
+            reverse=reverse,
+            # Skip the memory encoder when adding clicks or mask. We execute the memory encoder
+            # at the beginning of `propagate_in_video` (after user finalize their clicks). This
+            # allows us to enforce non-overlapping constraints on all objects before encoding
+            # them into memory.
+            run_mem_encoder=False,
+        )
+        # Add the output to the output dict (to be used as future memory)
+        obj_temp_output_dict[storage_key][frame_idx] = current_out
+
+        # Resize the output mask to the original video resolution
+        obj_ids = inference_state["obj_ids"]
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        return frame_idx, obj_ids, video_res_masks
+
+    def _get_orig_video_res_output(self, inference_state, any_res_masks):
+        """
+        Resize the object scores to the original video resolution (video_res_masks)
+        and apply non-overlapping constraints for final output.
+        """
+        device = inference_state["device"]
+        video_H = inference_state["video_height"]
+        video_W = inference_state["video_width"]
+        any_res_masks = any_res_masks.to(device, non_blocking=True)
+        if any_res_masks.shape[-2:] == (video_H, video_W):
+            video_res_masks = any_res_masks
+        else:
+            video_res_masks = torch.nn.functional.interpolate(
+                any_res_masks,
+                size=(video_H, video_W),
+                mode="bilinear",
+                align_corners=False,
+            )
+        if self.non_overlap_masks:
+            video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)
+        return any_res_masks, video_res_masks
+
+    def _consolidate_temp_output_across_obj(
+        self,
+        inference_state,
+        frame_idx,
+        is_cond,
+        run_mem_encoder,
+        consolidate_at_video_res=False,
+    ):
+        """
+        Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on
+        a frame into a single output for all objects, including
+        1) fill any missing objects either from `output_dict_per_obj` (if they exist in
+           `output_dict_per_obj` for this frame) or leave them as placeholder values
+           (if they don't exist in `output_dict_per_obj` for this frame);
+        2) if specified, rerun memory encoder after apply non-overlapping constraints
+           on the object scores.
+        """
+        batch_size = self._get_obj_num(inference_state)
+        storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+        # Optionally, we allow consolidating the temporary outputs at the original
+        # video resolution (to provide a better editing experience for mask prompts).
+        if consolidate_at_video_res:
+            assert not run_mem_encoder, "memory encoder cannot run at video resolution"
+            consolidated_H = inference_state["video_height"]
+            consolidated_W = inference_state["video_width"]
+            consolidated_mask_key = "pred_masks_video_res"
+        else:
+            consolidated_H = consolidated_W = self.image_size // 4
+            consolidated_mask_key = "pred_masks"
+
+        # Initialize `consolidated_out`. Its "maskmem_features" and "maskmem_pos_enc"
+        # will be added when rerunning the memory encoder after applying non-overlapping
+        # constraints to object scores. Its "pred_masks" are prefilled with a large
+        # negative value (NO_OBJ_SCORE) to represent missing objects.
+        consolidated_out = {
+            "maskmem_features": None,
+            "maskmem_pos_enc": None,
+            consolidated_mask_key: torch.full(
+                size=(batch_size, 1, consolidated_H, consolidated_W),
+                fill_value=NO_OBJ_SCORE,
+                dtype=torch.float32,
+                device=inference_state["storage_device"],
+            ),
+            "obj_ptr": torch.full(
+                size=(batch_size, self.hidden_dim),
+                fill_value=NO_OBJ_SCORE,
+                dtype=torch.float32,
+                device=inference_state["device"],
+            ),
+            "object_score_logits": torch.full(
+                size=(batch_size, 1),
+                # default to 10.0 for object_score_logits, i.e. assuming the object is
+                # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder`
+                fill_value=10.0,
+                dtype=torch.float32,
+                device=inference_state["device"],
+            ),
+        }
+        empty_mask_ptr = None
+        for obj_idx in range(batch_size):
+            obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx]
+            obj_output_dict = inference_state["output_dict_per_obj"][obj_idx]
+            out = obj_temp_output_dict[storage_key].get(frame_idx, None)
+            # If the object doesn't appear in "temp_output_dict_per_obj" on this frame,
+            # we fall back and look up its previous output in "output_dict_per_obj".
+            # We look up both "cond_frame_outputs" and "non_cond_frame_outputs" in
+            # "output_dict_per_obj" to find a previous output for this object.
+            if out is None:
+                out = obj_output_dict["cond_frame_outputs"].get(frame_idx, None)
+            if out is None:
+                out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx, None)
+            # If the object doesn't appear in "output_dict_per_obj" either, we skip it
+            # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE
+            # placeholder above) and set its object pointer to be a dummy pointer.
+            if out is None:
+                # Fill in dummy object pointers for those objects without any inputs or
+                # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,
+                # i.e. when we need to build the memory for tracking).
+                if run_mem_encoder:
+                    if empty_mask_ptr is None:
+                        empty_mask_ptr = self._get_empty_mask_ptr(
+                            inference_state, frame_idx
+                        )
+                    # fill object pointer with a dummy pointer (based on an empty mask)
+                    consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = empty_mask_ptr
+                continue
+            # Add the temporary object output mask to consolidated output mask
+            obj_mask = out["pred_masks"]
+            consolidated_pred_masks = consolidated_out[consolidated_mask_key]
+            if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:
+                consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask
+            else:
+                # Resize first if temporary object mask has a different resolution
+                resized_obj_mask = torch.nn.functional.interpolate(
+                    obj_mask,
+                    size=consolidated_pred_masks.shape[-2:],
+                    mode="bilinear",
+                    align_corners=False,
+                )
+                consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask
+            consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = out["obj_ptr"]
+            consolidated_out["object_score_logits"][obj_idx : obj_idx + 1] = out[
+                "object_score_logits"
+            ]
+
+        # Optionally, apply non-overlapping constraints on the consolidated scores
+        # and rerun the memory encoder
+        if run_mem_encoder:
+            device = inference_state["device"]
+            high_res_masks = torch.nn.functional.interpolate(
+                consolidated_out["pred_masks"].to(device, non_blocking=True),
+                size=(self.image_size, self.image_size),
+                mode="bilinear",
+                align_corners=False,
+            )
+            if self.non_overlap_masks_for_mem_enc:
+                high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)
+            maskmem_features, maskmem_pos_enc = self._run_memory_encoder(
+                inference_state=inference_state,
+                frame_idx=frame_idx,
+                batch_size=batch_size,
+                high_res_masks=high_res_masks,
+                object_score_logits=consolidated_out["object_score_logits"],
+                is_mask_from_pts=True,  # these frames are what the user interacted with
+            )
+            consolidated_out["maskmem_features"] = maskmem_features
+            consolidated_out["maskmem_pos_enc"] = maskmem_pos_enc
+
+        return consolidated_out
+
+    def _get_empty_mask_ptr(self, inference_state, frame_idx):
+        """Get a dummy object pointer based on an empty mask on the current frame."""
+        # A dummy (empty) mask with a single object
+        batch_size = 1
+        mask_inputs = torch.zeros(
+            (batch_size, 1, self.image_size, self.image_size),
+            dtype=torch.float32,
+            device=inference_state["device"],
+        )
+
+        # Retrieve correct image features
+        (
+            _,
+            _,
+            current_vision_feats,
+            current_vision_pos_embeds,
+            feat_sizes,
+        ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+        # Feed the empty mask and image feature above to get a dummy object pointer
+        current_out = self.track_step(
+            frame_idx=frame_idx,
+            is_init_cond_frame=True,
+            current_vision_feats=current_vision_feats,
+            current_vision_pos_embeds=current_vision_pos_embeds,
+            feat_sizes=feat_sizes,
+            point_inputs=None,
+            mask_inputs=mask_inputs,
+            output_dict={},
+            num_frames=inference_state["num_frames"],
+            track_in_reverse=False,
+            run_mem_encoder=False,
+            prev_sam_mask_logits=None,
+        )
+        return current_out["obj_ptr"]
+
+    @torch.no_grad()
+    def propagate_in_video_preflight(self, inference_state):
+        """Prepare inference_state and consolidate temporary outputs before tracking."""
+        # Tracking has started and we don't allow adding new objects until session is reset.
+        inference_state["tracking_has_started"] = True
+        batch_size = self._get_obj_num(inference_state)
+
+        # Consolidate per-object temporary outputs in "temp_output_dict_per_obj" and
+        # add them into "output_dict".
+        temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+        output_dict = inference_state["output_dict"]
+        # "consolidated_frame_inds" contains indices of those frames where consolidated
+        # temporary outputs have been added (either in this call or any previous calls
+        # to `propagate_in_video_preflight`).
+        consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+        for is_cond in [False, True]:
+            # Separately consolidate conditioning and non-conditioning temp outputs
+            storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
+            # Find all the frames that contain temporary outputs for any objects
+            # (these should be the frames that have just received clicks for mask inputs
+            # via `add_new_points_or_box` or `add_new_mask`)
+            temp_frame_inds = set()
+            for obj_temp_output_dict in temp_output_dict_per_obj.values():
+                temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())
+            consolidated_frame_inds[storage_key].update(temp_frame_inds)
+            # consolidate the temporary output across all objects on this frame
+            for frame_idx in temp_frame_inds:
+                consolidated_out = self._consolidate_temp_output_across_obj(
+                    inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True
+                )
+                # merge them into "output_dict" and also create per-object slices
+                output_dict[storage_key][frame_idx] = consolidated_out
+                self._add_output_per_object(
+                    inference_state, frame_idx, consolidated_out, storage_key
+                )
+                clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+                    self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+                )
+                if clear_non_cond_mem:
+                    # clear non-conditioning memory of the surrounding frames
+                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+
+            # clear temporary outputs in `temp_output_dict_per_obj`
+            for obj_temp_output_dict in temp_output_dict_per_obj.values():
+                obj_temp_output_dict[storage_key].clear()
+
+        # edge case: if an output is added to "cond_frame_outputs", we remove any prior
+        # output on the same frame in "non_cond_frame_outputs"
+        for frame_idx in output_dict["cond_frame_outputs"]:
+            output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+        for obj_output_dict in inference_state["output_dict_per_obj"].values():
+            for frame_idx in obj_output_dict["cond_frame_outputs"]:
+                obj_output_dict["non_cond_frame_outputs"].pop(frame_idx, None)
+        for frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+            assert frame_idx in output_dict["cond_frame_outputs"]
+            consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+
+        # Make sure that the frame indices in "consolidated_frame_inds" are exactly those frames
+        # with either points or mask inputs (which should be true under a correct workflow).
+        all_consolidated_frame_inds = (
+            consolidated_frame_inds["cond_frame_outputs"]
+            | consolidated_frame_inds["non_cond_frame_outputs"]
+        )
+        input_frames_inds = set()
+        for point_inputs_per_frame in inference_state["point_inputs_per_obj"].values():
+            input_frames_inds.update(point_inputs_per_frame.keys())
+        for mask_inputs_per_frame in inference_state["mask_inputs_per_obj"].values():
+            input_frames_inds.update(mask_inputs_per_frame.keys())
+        assert all_consolidated_frame_inds == input_frames_inds
+
+    @torch.no_grad()
+    def propagate_in_video(
+        self,
+        inference_state,
+        start_frame_idx=None,
+        max_frame_num_to_track=None,
+        reverse=False,
+    ):
+        """Propagate the input points across frames to track in the entire video."""
+        self.propagate_in_video_preflight(inference_state)
+
+        output_dict = inference_state["output_dict"]
+        consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+        obj_ids = inference_state["obj_ids"]
+        num_frames = inference_state["num_frames"]
+        batch_size = self._get_obj_num(inference_state)
+        if len(output_dict["cond_frame_outputs"]) == 0:
+            raise RuntimeError("No points are provided; please add points first")
+        clear_non_cond_mem = self.clear_non_cond_mem_around_input and (
+            self.clear_non_cond_mem_for_multi_obj or batch_size <= 1
+        )
+
+        # set start index, end index, and processing order
+        if start_frame_idx is None:
+            # default: start from the earliest frame with input points
+            start_frame_idx = min(output_dict["cond_frame_outputs"])
+        if max_frame_num_to_track is None:
+            # default: track all the frames in the video
+            max_frame_num_to_track = num_frames
+        if reverse:
+            end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)
+            if start_frame_idx > 0:
+                processing_order = range(start_frame_idx, end_frame_idx - 1, -1)
+            else:
+                processing_order = []  # skip reverse tracking if starting from frame 0
+        else:
+            end_frame_idx = min(
+                start_frame_idx + max_frame_num_to_track, num_frames - 1
+            )
+            processing_order = range(start_frame_idx, end_frame_idx + 1)
+
+        for frame_idx in tqdm(processing_order, desc="propagate in video"):
+            # We skip those frames already in consolidated outputs (these are frames
+            # that received input clicks or mask). Note that we cannot directly run
+            # batched forward on them via `_run_single_frame_inference` because the
+            # number of clicks on each object might be different.
+            if frame_idx in consolidated_frame_inds["cond_frame_outputs"]:
+                storage_key = "cond_frame_outputs"
+                current_out = output_dict[storage_key][frame_idx]
+                pred_masks = current_out["pred_masks"]
+                if clear_non_cond_mem:
+                    # clear non-conditioning memory of the surrounding frames
+                    self._clear_non_cond_mem_around_input(inference_state, frame_idx)
+            elif frame_idx in consolidated_frame_inds["non_cond_frame_outputs"]:
+                storage_key = "non_cond_frame_outputs"
+                current_out = output_dict[storage_key][frame_idx]
+                pred_masks = current_out["pred_masks"]
+            else:
+                storage_key = "non_cond_frame_outputs"
+                current_out, pred_masks = self._run_single_frame_inference(
+                    inference_state=inference_state,
+                    output_dict=output_dict,
+                    frame_idx=frame_idx,
+                    batch_size=batch_size,
+                    is_init_cond_frame=False,
+                    point_inputs=None,
+                    mask_inputs=None,
+                    reverse=reverse,
+                    run_mem_encoder=True,
+                )
+                output_dict[storage_key][frame_idx] = current_out
+            # Create slices of per-object outputs for subsequent interaction with each
+            # individual object after tracking.
+            self._add_output_per_object(
+                inference_state, frame_idx, current_out, storage_key
+            )
+            inference_state["frames_already_tracked"][frame_idx] = {"reverse": reverse}
+
+            # Resize the output mask to the original video resolution (we directly use
+            # the mask scores on GPU for output to avoid any CPU conversion in between)
+            _, video_res_masks = self._get_orig_video_res_output(
+                inference_state, pred_masks
+            )
+            yield frame_idx, obj_ids, video_res_masks
+
+    def _add_output_per_object(
+        self, inference_state, frame_idx, current_out, storage_key
+    ):
+        """
+        Split a multi-object output into per-object output slices and add them into
+        `output_dict_per_obj`. The resulting slices share the same tensor storage.
+        """
+        maskmem_features = current_out["maskmem_features"]
+        assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)
+
+        maskmem_pos_enc = current_out["maskmem_pos_enc"]
+        assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)
+
+        output_dict_per_obj = inference_state["output_dict_per_obj"]
+        for obj_idx, obj_output_dict in output_dict_per_obj.items():
+            obj_slice = slice(obj_idx, obj_idx + 1)
+            obj_out = {
+                "maskmem_features": None,
+                "maskmem_pos_enc": None,
+                "pred_masks": current_out["pred_masks"][obj_slice],
+                "obj_ptr": current_out["obj_ptr"][obj_slice],
+                "object_score_logits": current_out["object_score_logits"][obj_slice],
+            }
+            if maskmem_features is not None:
+                obj_out["maskmem_features"] = maskmem_features[obj_slice]
+            if maskmem_pos_enc is not None:
+                obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc]
+            obj_output_dict[storage_key][frame_idx] = obj_out
+
+    @torch.no_grad()
+    def clear_all_prompts_in_frame(
+        self, inference_state, frame_idx, obj_id, need_output=True
+    ):
+        """Remove all input points or mask in a specific frame for a given object."""
+        obj_idx = self._obj_id_to_idx(inference_state, obj_id)
+
+        # Clear the conditioning information on the given frame
+        inference_state["point_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+        inference_state["mask_inputs_per_obj"][obj_idx].pop(frame_idx, None)
+
+        temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+        temp_output_dict_per_obj[obj_idx]["cond_frame_outputs"].pop(frame_idx, None)
+        temp_output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].pop(frame_idx, None)
+
+        # Check and see if there are still any inputs left on this frame
+        batch_size = self._get_obj_num(inference_state)
+        frame_has_input = False
+        for obj_idx2 in range(batch_size):
+            if frame_idx in inference_state["point_inputs_per_obj"][obj_idx2]:
+                frame_has_input = True
+                break
+            if frame_idx in inference_state["mask_inputs_per_obj"][obj_idx2]:
+                frame_has_input = True
+                break
+
+        # If this frame has no remaining inputs for any objects, we further clear its
+        # conditioning frame status
+        if not frame_has_input:
+            output_dict = inference_state["output_dict"]
+            consolidated_frame_inds = inference_state["consolidated_frame_inds"]
+            consolidated_frame_inds["cond_frame_outputs"].discard(frame_idx)
+            consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx)
+            # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)
+            out = output_dict["cond_frame_outputs"].pop(frame_idx, None)
+            if out is not None:
+                # The frame is not a conditioning frame anymore since it's not receiving inputs,
+                # so we "downgrade" its output (if exists) to a non-conditioning frame output.
+                output_dict["non_cond_frame_outputs"][frame_idx] = out
+                inference_state["frames_already_tracked"].pop(frame_idx, None)
+            # Similarly, do it for the sliced output on each object.
+            for obj_idx2 in range(batch_size):
+                obj_output_dict = inference_state["output_dict_per_obj"][obj_idx2]
+                obj_out = obj_output_dict["cond_frame_outputs"].pop(frame_idx, None)
+                if obj_out is not None:
+                    obj_output_dict["non_cond_frame_outputs"][frame_idx] = obj_out
+
+            # If all the conditioning frames have been removed, we also clear the tracking outputs
+            if len(output_dict["cond_frame_outputs"]) == 0:
+                self._reset_tracking_results(inference_state)
+
+        if not need_output:
+            return
+        # Finally, output updated masks per object (after removing the inputs above)
+        obj_ids = inference_state["obj_ids"]
+        is_cond = any(
+            frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+            for obj_temp_output_dict in temp_output_dict_per_obj.values()
+        )
+        consolidated_out = self._consolidate_temp_output_across_obj(
+            inference_state,
+            frame_idx,
+            is_cond=is_cond,
+            run_mem_encoder=False,
+            consolidate_at_video_res=True,
+        )
+        _, video_res_masks = self._get_orig_video_res_output(
+            inference_state, consolidated_out["pred_masks_video_res"]
+        )
+        return frame_idx, obj_ids, video_res_masks
+
+    @torch.no_grad()
+    def reset_state(self, inference_state):
+        """Remove all input points or mask in all frames throughout the video."""
+        self._reset_tracking_results(inference_state)
+        # Remove all object ids
+        inference_state["obj_id_to_idx"].clear()
+        inference_state["obj_idx_to_id"].clear()
+        inference_state["obj_ids"].clear()
+        inference_state["point_inputs_per_obj"].clear()
+        inference_state["mask_inputs_per_obj"].clear()
+        inference_state["output_dict_per_obj"].clear()
+        inference_state["temp_output_dict_per_obj"].clear()
+
+    def _reset_tracking_results(self, inference_state):
+        """Reset all tracking inputs and results across the videos."""
+        for v in inference_state["point_inputs_per_obj"].values():
+            v.clear()
+        for v in inference_state["mask_inputs_per_obj"].values():
+            v.clear()
+        for v in inference_state["output_dict_per_obj"].values():
+            v["cond_frame_outputs"].clear()
+            v["non_cond_frame_outputs"].clear()
+        for v in inference_state["temp_output_dict_per_obj"].values():
+            v["cond_frame_outputs"].clear()
+            v["non_cond_frame_outputs"].clear()
+        inference_state["output_dict"]["cond_frame_outputs"].clear()
+        inference_state["output_dict"]["non_cond_frame_outputs"].clear()
+        inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear()
+        inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear()
+        inference_state["tracking_has_started"] = False
+        inference_state["frames_already_tracked"].clear()
+
+    def _get_image_feature(self, inference_state, frame_idx, batch_size):
+        """Compute the image features on a given frame."""
+        # Look up in the cache first
+        image, backbone_out = inference_state["cached_features"].get(
+            frame_idx, (None, None)
+        )
+        if backbone_out is None:
+            # Cache miss -- we will run inference on a single image
+            device = inference_state["device"]
+            image = inference_state["images"][frame_idx].to(device).float().unsqueeze(0)
+            backbone_out = self.forward_image(image)
+            # Cache the most recent frame's feature (for repeated interactions with
+            # a frame; we can use an LRU cache for more frames in the future).
+            inference_state["cached_features"] = {frame_idx: (image, backbone_out)}
+
+        # expand the features to have the same dimension as the number of objects
+        expanded_image = image.expand(batch_size, -1, -1, -1)
+        expanded_backbone_out = {
+            "backbone_fpn": backbone_out["backbone_fpn"].copy(),
+            "vision_pos_enc": backbone_out["vision_pos_enc"].copy(),
+        }
+        for i, feat in enumerate(expanded_backbone_out["backbone_fpn"]):
+            expanded_backbone_out["backbone_fpn"][i] = feat.expand(
+                batch_size, -1, -1, -1
+            )
+        for i, pos in enumerate(expanded_backbone_out["vision_pos_enc"]):
+            pos = pos.expand(batch_size, -1, -1, -1)
+            expanded_backbone_out["vision_pos_enc"][i] = pos
+
+        features = self._prepare_backbone_features(expanded_backbone_out)
+        features = (expanded_image,) + features
+        return features
+
+    @torch.autograd.profiler.record_function("_run_single_frame_inference")
+    def _run_single_frame_inference(
+        self,
+        inference_state,
+        output_dict,
+        frame_idx,
+        batch_size,
+        is_init_cond_frame,
+        point_inputs,
+        mask_inputs,
+        reverse,
+        run_mem_encoder,
+        prev_sam_mask_logits=None,
+    ):
+        """Run tracking on a single frame based on current inputs and previous memory."""
+        # Retrieve correct image features
+        (
+            _,
+            _,
+            current_vision_feats,
+            current_vision_pos_embeds,
+            feat_sizes,
+        ) = self._get_image_feature(inference_state, frame_idx, batch_size)
+
+        # point and mask should not appear as input simultaneously on the same frame
+        assert point_inputs is None or mask_inputs is None
+        current_out = self.track_step(
+            frame_idx=frame_idx,
+            is_init_cond_frame=is_init_cond_frame,
+            current_vision_feats=current_vision_feats,
+            current_vision_pos_embeds=current_vision_pos_embeds,
+            feat_sizes=feat_sizes,
+            point_inputs=point_inputs,
+            mask_inputs=mask_inputs,
+            output_dict=output_dict,
+            num_frames=inference_state["num_frames"],
+            track_in_reverse=reverse,
+            run_mem_encoder=run_mem_encoder,
+            prev_sam_mask_logits=prev_sam_mask_logits,
+        )
+
+        # optionally offload the output to CPU memory to save GPU space
+        storage_device = inference_state["storage_device"]
+        maskmem_features = current_out["maskmem_features"]
+        if maskmem_features is not None:
+            maskmem_features = maskmem_features.to(torch.bfloat16)
+            maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+        pred_masks_gpu = current_out["pred_masks"]
+        # potentially fill holes in the predicted masks
+        if self.fill_hole_area > 0:
+            pred_masks_gpu = fill_holes_in_mask_scores(
+                pred_masks_gpu, self.fill_hole_area
+            )
+        pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)
+        # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+        maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)
+        # object pointer is a small tensor, so we always keep it on GPU memory for fast access
+        obj_ptr = current_out["obj_ptr"]
+        object_score_logits = current_out["object_score_logits"]
+        # make a compact version of this frame's output to reduce the state size
+        compact_current_out = {
+            "maskmem_features": maskmem_features,
+            "maskmem_pos_enc": maskmem_pos_enc,
+            "pred_masks": pred_masks,
+            "obj_ptr": obj_ptr,
+            "object_score_logits": object_score_logits,
+        }
+        return compact_current_out, pred_masks_gpu
+
+    def _run_memory_encoder(
+        self,
+        inference_state,
+        frame_idx,
+        batch_size,
+        high_res_masks,
+        object_score_logits,
+        is_mask_from_pts,
+    ):
+        """
+        Run the memory encoder on `high_res_masks`. This is usually after applying
+        non-overlapping constraints to object scores. Since their scores changed, their
+        memory also need to be computed again with the memory encoder.
+        """
+        # Retrieve correct image features
+        _, _, current_vision_feats, _, feat_sizes = self._get_image_feature(
+            inference_state, frame_idx, batch_size
+        )
+        maskmem_features, maskmem_pos_enc = self._encode_new_memory(
+            current_vision_feats=current_vision_feats,
+            feat_sizes=feat_sizes,
+            pred_masks_high_res=high_res_masks,
+            object_score_logits=object_score_logits,
+            is_mask_from_pts=is_mask_from_pts,
+        )
+
+        # optionally offload the output to CPU memory to save GPU space
+        storage_device = inference_state["storage_device"]
+        maskmem_features = maskmem_features.to(torch.bfloat16)
+        maskmem_features = maskmem_features.to(storage_device, non_blocking=True)
+        # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+        maskmem_pos_enc = self._get_maskmem_pos_enc(
+            inference_state, {"maskmem_pos_enc": maskmem_pos_enc}
+        )
+        return maskmem_features, maskmem_pos_enc
+
+    def _get_maskmem_pos_enc(self, inference_state, current_out):
+        """
+        `maskmem_pos_enc` is the same across frames and objects, so we cache it as
+        a constant in the inference session to reduce session storage size.
+        """
+        model_constants = inference_state["constants"]
+        # "out_maskmem_pos_enc" should be either a list of tensors or None
+        out_maskmem_pos_enc = current_out["maskmem_pos_enc"]
+        if out_maskmem_pos_enc is not None:
+            if "maskmem_pos_enc" not in model_constants:
+                assert isinstance(out_maskmem_pos_enc, list)
+                # only take the slice for one object, since it's same across objects
+                maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]
+                model_constants["maskmem_pos_enc"] = maskmem_pos_enc
+            else:
+                maskmem_pos_enc = model_constants["maskmem_pos_enc"]
+            # expand the cached maskmem_pos_enc to the actual batch size
+            batch_size = out_maskmem_pos_enc[0].size(0)
+            expanded_maskmem_pos_enc = [
+                x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc
+            ]
+        else:
+            expanded_maskmem_pos_enc = None
+        return expanded_maskmem_pos_enc
+
+    @torch.no_grad()
+    def remove_object(self, inference_state, obj_id, strict=False, need_output=True):
+        """
+        Remove an object id from the tracking state. If strict is True, we check whether
+        the object id actually exists and raise an error if it doesn't exist.
+        """
+        old_obj_idx_to_rm = inference_state["obj_id_to_idx"].get(obj_id, None)
+        updated_frames = []
+        # Check whether this object_id to remove actually exists and possibly raise an error.
+        if old_obj_idx_to_rm is None:
+            if not strict:
+                return inference_state["obj_ids"], updated_frames
+            raise RuntimeError(
+                f"Cannot remove object id {obj_id} as it doesn't exist. "
+                f"All existing object ids: {inference_state['obj_ids']}."
+            )
+
+        # If this is the only remaining object id, we simply reset the state.
+        if len(inference_state["obj_id_to_idx"]) == 1:
+            self.reset_state(inference_state)
+            return inference_state["obj_ids"], updated_frames
+
+        # There are still remaining objects after removing this object id. In this case,
+        # we need to delete the object storage from inference state tensors.
+        # Step 0: clear the input on those frames where this object id has point or mask input
+        # (note that this step is required as it might downgrade conditioning frames to
+        # non-conditioning ones)
+        obj_input_frames_inds = set()
+        obj_input_frames_inds.update(
+            inference_state["point_inputs_per_obj"][old_obj_idx_to_rm]
+        )
+        obj_input_frames_inds.update(
+            inference_state["mask_inputs_per_obj"][old_obj_idx_to_rm]
+        )
+        for frame_idx in obj_input_frames_inds:
+            self.clear_all_prompts_in_frame(
+                inference_state, frame_idx, obj_id, need_output=False
+            )
+
+        # Step 1: Update the object id mapping (note that it must be done after Step 0,
+        # since Step 0 still requires the old object id mappings in inference_state)
+        old_obj_ids = inference_state["obj_ids"]
+        old_obj_inds = list(range(len(old_obj_ids)))
+        remain_old_obj_inds = old_obj_inds.copy()
+        remain_old_obj_inds.remove(old_obj_idx_to_rm)
+        new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]
+        new_obj_inds = list(range(len(new_obj_ids)))
+        # build new mappings
+        old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))
+        inference_state["obj_id_to_idx"] = dict(zip(new_obj_ids, new_obj_inds))
+        inference_state["obj_idx_to_id"] = dict(zip(new_obj_inds, new_obj_ids))
+        inference_state["obj_ids"] = new_obj_ids
+
+        # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.
+        # (note that "consolidated_frame_inds" doesn't need to be updated in this step as
+        # it's already handled in Step 0)
+        def _map_keys(container):
+            new_kvs = []
+            for k in old_obj_inds:
+                v = container.pop(k)
+                if k in old_idx_to_new_idx:
+                    new_kvs.append((old_idx_to_new_idx[k], v))
+            container.update(new_kvs)
+
+        _map_keys(inference_state["point_inputs_per_obj"])
+        _map_keys(inference_state["mask_inputs_per_obj"])
+        _map_keys(inference_state["output_dict_per_obj"])
+        _map_keys(inference_state["temp_output_dict_per_obj"])
+
+        # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.
+        def _slice_state(output_dict, storage_key):
+            for frame_idx, out in output_dict[storage_key].items():
+                out["maskmem_features"] = out["maskmem_features"][remain_old_obj_inds]
+                out["maskmem_pos_enc"] = [
+                    x[remain_old_obj_inds] for x in out["maskmem_pos_enc"]
+                ]
+                # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it
+                out["maskmem_pos_enc"] = self._get_maskmem_pos_enc(inference_state, out)
+                out["pred_masks"] = out["pred_masks"][remain_old_obj_inds]
+                out["obj_ptr"] = out["obj_ptr"][remain_old_obj_inds]
+                out["object_score_logits"] = out["object_score_logits"][
+                    remain_old_obj_inds
+                ]
+                # also update the per-object slices
+                self._add_output_per_object(
+                    inference_state, frame_idx, out, storage_key
+                )
+
+        _slice_state(inference_state["output_dict"], "cond_frame_outputs")
+        _slice_state(inference_state["output_dict"], "non_cond_frame_outputs")
+
+        # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which
+        # could show an updated mask for objects previously occluded by the object being removed
+        if need_output:
+            temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"]
+            for frame_idx in obj_input_frames_inds:
+                is_cond = any(
+                    frame_idx in obj_temp_output_dict["cond_frame_outputs"]
+                    for obj_temp_output_dict in temp_output_dict_per_obj.values()
+                )
+                consolidated_out = self._consolidate_temp_output_across_obj(
+                    inference_state,
+                    frame_idx,
+                    is_cond=is_cond,
+                    run_mem_encoder=False,
+                    consolidate_at_video_res=True,
+                )
+                _, video_res_masks = self._get_orig_video_res_output(
+                    inference_state, consolidated_out["pred_masks_video_res"]
+                )
+                updated_frames.append((frame_idx, video_res_masks))
+
+        return inference_state["obj_ids"], updated_frames
+
+    def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):
+        """
+        Remove the non-conditioning memory around the input frame. When users provide
+        correction clicks, the surrounding frames' non-conditioning memories can still
+        contain outdated object appearance information and could confuse the model.
+
+        This method clears those non-conditioning memories surrounding the interacted
+        frame to avoid giving the model both old and new information about the object.
+        """
+        r = self.memory_temporal_stride_for_eval
+        frame_idx_begin = frame_idx - r * self.num_maskmem
+        frame_idx_end = frame_idx + r * self.num_maskmem
+        output_dict = inference_state["output_dict"]
+        non_cond_frame_outputs = output_dict["non_cond_frame_outputs"]
+        for t in range(frame_idx_begin, frame_idx_end + 1):
+            non_cond_frame_outputs.pop(t, None)
+            for obj_output_dict in inference_state["output_dict_per_obj"].values():
+                obj_output_dict["non_cond_frame_outputs"].pop(t, None)

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/utils/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/utils/amg.py

@@ -0,0 +1,523 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from copy import deepcopy
+from dataclasses import dataclass
+from itertools import product
+from typing import Any, Dict, Generator, ItemsView, List, Tuple
+
+import numpy as np
+import torch
+
+
+@dataclass
+class RLEData:
+    alt_lens_nt: torch.Tensor
+    counts_init: torch.Tensor
+    b: int
+    h: int
+    w: int
+
+    def __len__(self):
+        return self.b
+
+
+def nt_index_select_dim_0(nt, index):
+    values = nt.values()
+    offsets = nt.offsets()
+    lengths = offsets.diff()
+    offsets_index = offsets[index].cpu()
+    lengths_index = lengths[index].cpu()
+    indices = [o + torch.arange(l) for (o, l) in zip(offsets_index, lengths_index)]
+    indices = torch.cat(indices).to(values.device)
+    values_index = torch.index_select(values, 0, indices)
+    lengths_index = lengths_index.to(values.device)
+    return torch.nested.nested_tensor_from_jagged(values_index, lengths=lengths_index)
+
+
+def nt_cat_dim_0(nts: List[torch.Tensor]):
+    all_values = []
+    all_lengths = []
+    for nt in nts:
+        all_values.append(nt.values())
+        all_lengths.append(nt.offsets().diff())
+    new_values = torch.cat(all_values)
+    new_lengths = torch.cat(all_lengths)
+    return torch.nested.nested_tensor_from_jagged(new_values, lengths=new_lengths)
+
+
+# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py
+class MaskData:
+    """
+    A structure for storing masks and their related data in batched format.
+    Implements basic filtering and concatenation.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        for v in kwargs.values():
+            assert isinstance(v, (list, np.ndarray, torch.Tensor, RLEData)), (
+                "MaskData only supports list, numpy arrays, and torch tensors."
+            )
+        self._stats = dict(**kwargs)
+
+    def __setitem__(self, key: str, item: Any) -> None:
+        assert isinstance(item, (list, np.ndarray, torch.Tensor, RLEData)), (
+            "MaskData only supports list, numpy arrays, and torch tensors."
+        )
+        self._stats[key] = item
+
+    def __delitem__(self, key: str) -> None:
+        del self._stats[key]
+
+    def __getitem__(self, key: str) -> Any:
+        return self._stats[key]
+
+    def items(self) -> ItemsView[str, Any]:
+        return self._stats.items()
+
+    def filter(self, keep: torch.Tensor) -> None:
+        for k, v in self._stats.items():
+            if v is None:
+                self._stats[k] = None
+            elif isinstance(v, torch.Tensor):
+                # self._stats[k] = v[torch.as_tensor(keep, device=v.device)]
+                self._stats[k] = v[keep]
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = v[keep.detach().cpu().numpy()]
+            elif isinstance(v, list) and keep.dtype == torch.bool:
+                self._stats[k] = [a for i, a in enumerate(v) if keep[i]]
+            elif isinstance(v, list):
+                self._stats[k] = [v[i] for i in keep]
+            elif isinstance(v, RLEData):
+                new_alt_lens_nt = nt_index_select_dim_0(v.alt_lens_nt, keep)
+                self._stats[k] = RLEData(
+                    alt_lens_nt=new_alt_lens_nt,
+                    counts_init=v.counts_init[keep],
+                    b=new_alt_lens_nt.size(0),
+                    h=v.h,
+                    w=v.w,
+                )
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def cat(self, new_stats: "MaskData") -> None:
+        for k, v in new_stats.items():
+            if k not in self._stats or self._stats[k] is None:
+                self._stats[k] = deepcopy(v)
+            elif isinstance(v, torch.Tensor):
+                self._stats[k] = torch.cat([self._stats[k], v], dim=0)
+            elif isinstance(v, np.ndarray):
+                self._stats[k] = np.concatenate([self._stats[k], v], axis=0)
+            elif isinstance(v, list):
+                self._stats[k] = self._stats[k] + deepcopy(v)
+            elif isinstance(v, RLEData):
+                assert self._stats[k].h == v.h
+                assert self._stats[k].w == v.w
+                self._stats[k] = RLEData(
+                    alt_lens_nt=nt_cat_dim_0(
+                        [self._stats[k].alt_lens_nt, v.alt_lens_nt]
+                    ),
+                    counts_init=torch.cat([self._stats[k].counts_init, v.counts_init]),
+                    b=self._stats[k].b + v.b,
+                    h=v.h,
+                    w=v.w,
+                )
+            else:
+                raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.")
+
+    def to_numpy(self) -> None:
+        for k, v in self._stats.items():
+            if isinstance(v, torch.Tensor):
+                self._stats[k] = v.float().detach().cpu().numpy()
+
+
+def is_box_near_crop_edge_torch(
+    boxes: torch.Tensor,
+    crop_box: List[int],
+    crop_box_torch: torch.Tensor,
+    orig_box_torch: torch.Tensor,
+    atol: float = 20.0,
+) -> torch.Tensor:
+    """Filter masks at the edge of a crop, but not at the edge of the original image."""
+    boxes = uncrop_boxes_xyxy(boxes, crop_box).float()
+    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
+    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
+    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
+    return torch.any(near_crop_edge, dim=1)
+
+
+def is_box_near_crop_edge(
+    boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0
+) -> torch.Tensor:
+    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
+    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)
+    return is_box_near_crop_edge_torch(
+        boxes, crop_box, crop_box_torch, orig_box_torch, atol
+    )
+
+
+def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:
+    box_xywh = deepcopy(box_xyxy)
+    box_xywh[2] = box_xywh[2] - box_xywh[0]
+    box_xywh[3] = box_xywh[3] - box_xywh[1]
+    return box_xywh
+
+
+def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
+    assert len(args) > 0 and all(len(a) == len(args[0]) for a in args), (
+        "Batched iteration must have inputs of all the same size."
+    )
+    n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
+    for b in range(n_batches):
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
+
+
+def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    """
+    Encodes masks to an uncompressed RLE, in the format expected by
+    pycoco tools.
+    """
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    change_indices = diff.nonzero()
+
+    # Encode run length
+    out = []
+    for i in range(b):
+        cur_idxs = change_indices[change_indices[:, 0] == i, 1]
+        cur_idxs = torch.cat(
+            [
+                torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),
+                cur_idxs + 1,
+                torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),
+            ]
+        )
+        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
+        counts = [] if tensor[i, 0] == 0 else [0]
+        counts.extend(btw_idxs.detach().cpu().tolist())
+        out.append({"size": [h, w], "counts": counts})
+    return out
+
+
+def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
+    """Compute a binary mask from an uncompressed RLE."""
+    h, w = rle["size"]
+    mask = np.empty(h * w, dtype=bool)
+    idx = 0
+    parity = False
+    for count in rle["counts"]:
+        mask[idx : idx + count] = parity
+        idx += count
+        parity ^= True
+    mask = mask.reshape(w, h)
+    return mask.transpose()  # Put in C order
+
+
+# @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.
+    """
+    # Put in fortran order and flatten h,w
+    b, h, w = tensor.shape
+    tensor = tensor.permute(0, 2, 1).flatten(1)
+
+    # Compute change indices
+    diff = tensor[:, 1:] ^ tensor[:, :-1]
+    # 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)
+    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)
+
+    alt_lens = diff.sum(dim=1)
+
+    all_cur_idx = change_indices[:, 1]
+    if all_cur_idx.numel() == 0:
+        all_cur_idx_0 = all_cur_idx
+        all_cur_idx_1 = all_cur_idx
+    else:
+        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
+
+    alt_lens_nt = torch.nested.nested_tensor_from_jagged(all_btw_idx, lengths=alt_lens)
+    # Encode run length
+    counts_init = tensor[:, 0] == 0
+    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"):
+        # NOTE: While we could operate on less chunks, a set number of chunks prevents recompilations
+        # 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()
+        num_chunks = 8
+        assert num_chunks >= ((diff.numel() + 2147483646) // 2147483646), (
+            "Needed more chunks than expected."
+        )
+        change_indices = torch.cat([d.nonzero() for d in diff.chunk(num_chunks)])
+    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):
+    with torch.autograd.profiler.record_function(
+        "mask_to_rle_pytorch_2: Encode run length"
+    ):
+        out = []
+        alt_lens = rle_data.alt_lens_nt.offsets().diff()
+        all_btw_idx = rle_data.alt_lens_nt.values()
+        alt_lens = alt_lens.tolist()
+        all_btw_idx = all_btw_idx.tolist()
+        counts_init = rle_data.counts_init.tolist()
+        offset = 0
+        for i, ci in zip(range(rle_data.b), counts_init):
+            btw_idxs = all_btw_idx[offset : offset + alt_lens[i]][:-1]
+            offset += alt_lens[i]
+            counts = [] if ci else [0]
+            counts.extend(btw_idxs)
+            out.append({"size": [rle_data.h, rle_data.w], "counts": counts})
+
+    return out
+
+
+def mask_to_rle_pytorch_2(tensor: torch.Tensor) -> List[Dict[str, Any]]:
+    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:
+    return sum(rle["counts"][1::2])
+
+
+# TODO: Turn this on if you can mitigate recompiles!
+# @torch.compile(fullgraph=True, dynamic=True)
+def calculate_stability_score(
+    masks: torch.Tensor, mask_threshold: float, threshold_offset: float
+) -> torch.Tensor:
+    """
+    Computes the stability score for a batch of masks. The stability
+    score is the IoU between the binary masks obtained by thresholding
+    the predicted mask logits at high and low values.
+    """
+    # One mask is always contained inside the other.
+    # Save memory by preventing unnecessary cast to torch.int64
+    intersections = (
+        (masks > (mask_threshold + threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    unions = (
+        (masks > (mask_threshold - threshold_offset))
+        .sum(-1, dtype=torch.int16)
+        .sum(-1, dtype=torch.int32)
+    )
+    return intersections / unions
+
+
+def build_point_grid(n_per_side: int) -> np.ndarray:
+    """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
+    offset = 1 / (2 * n_per_side)
+    points_one_side = np.linspace(offset, 1 - offset, n_per_side)
+    points_x = np.tile(points_one_side[None, :], (n_per_side, 1))
+    points_y = np.tile(points_one_side[:, None], (1, n_per_side))
+    points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)
+    return points
+
+
+def build_all_layer_point_grids(
+    n_per_side: int, n_layers: int, scale_per_layer: int
+) -> List[np.ndarray]:
+    """Generates point grids for all crop layers."""
+    points_by_layer = []
+    for i in range(n_layers + 1):
+        n_points = int(n_per_side / (scale_per_layer**i))
+        points_by_layer.append(build_point_grid(n_points))
+    return points_by_layer
+
+
+def generate_crop_boxes(
+    im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float
+) -> Tuple[List[List[int]], List[int]]:
+    """
+    Generates a list of crop boxes of different sizes. Each layer
+    has (2**i)**2 boxes for the ith layer.
+    """
+    crop_boxes, layer_idxs = [], []
+    im_h, im_w = im_size
+    short_side = min(im_h, im_w)
+
+    # Original image
+    crop_boxes.append([0, 0, im_w, im_h])
+    layer_idxs.append(0)
+
+    def crop_len(orig_len, n_crops, overlap):
+        return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))
+
+    for i_layer in range(n_layers):
+        n_crops_per_side = 2 ** (i_layer + 1)
+        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))
+
+        crop_w = crop_len(im_w, n_crops_per_side, overlap)
+        crop_h = crop_len(im_h, n_crops_per_side, overlap)
+
+        crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]
+        crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]
+
+        # Crops in XYWH format
+        for x0, y0 in product(crop_box_x0, crop_box_y0):
+            box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]
+            crop_boxes.append(box)
+            layer_idxs.append(i_layer + 1)
+
+    return crop_boxes, layer_idxs
+
+
+def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0, x0, y0]]).pin_memory()
+    offset = offset.to(device=boxes.device, non_blocking=True)
+    # Check if boxes has a channel dimension
+    if len(boxes.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return boxes + offset
+
+
+def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:
+    x0, y0, _, _ = crop_box
+    offset = torch.tensor([[x0, y0]]).pin_memory()
+    offset = offset.to(device=points.device, non_blocking=True)
+    # Check if points has a channel dimension
+    if len(points.shape) == 3:
+        offset = offset.unsqueeze(1)
+    return points + offset
+
+
+def uncrop_masks(
+    masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int
+) -> torch.Tensor:
+    x0, y0, x1, y1 = crop_box
+    if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:
+        return masks
+    # Coordinate transform masks
+    pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)
+    pad = (x0, pad_x - x0, y0, pad_y - y0)
+    return torch.nn.functional.pad(masks, pad, value=0)
+
+
+def remove_small_regions(
+    mask: np.ndarray, area_thresh: float, mode: str
+) -> Tuple[np.ndarray, bool]:
+    """
+    Removes small disconnected regions and holes in a mask. Returns the
+    mask and an indicator of if the mask has been modified.
+    """
+    import cv2  # type: ignore
+
+    assert mode in ["holes", "islands"]
+    correct_holes = mode == "holes"
+    working_mask = (correct_holes ^ mask).astype(np.uint8)
+    n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
+    sizes = stats[:, -1][1:]  # Row 0 is background label
+    small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
+    if len(small_regions) == 0:
+        return mask, False
+    fill_labels = [0] + small_regions
+    if not correct_holes:
+        fill_labels = [i for i in range(n_labels) if i not in fill_labels]
+        # If every region is below threshold, keep largest
+        if len(fill_labels) == 0:
+            fill_labels = [int(np.argmax(sizes)) + 1]
+    mask = np.isin(regions, fill_labels)
+    return mask, True
+
+
+def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:
+    from pycocotools import mask as mask_utils  # type: ignore
+
+    h, w = uncompressed_rle["size"]
+    rle = mask_utils.frPyObjects(uncompressed_rle, h, w)
+    rle["counts"] = rle["counts"].decode("utf-8")  # Necessary to serialize with json
+    return rle
+
+
+# TODO: Turn this on if you can mitigate recompiles!
+# @torch.compile(fullgraph=True, dynamic=True)
+def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:
+    """
+    Calculates boxes in XYXY format around masks. Return [0,0,0,0] for
+    an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.
+    """
+    # # torch.max below raises an error on empty inputs, just skip in this case
+    # if torch.numel(masks) == 0:
+    #     return torch.zeros(*masks.shape[:-2], 4, device=masks.device)
+
+    # Normalize shape to CxHxW
+    shape = masks.shape
+    h, w = shape[-2:]
+    if len(shape) > 2:
+        masks = masks.flatten(0, -3)
+    else:
+        masks = masks.unsqueeze(0)
+
+    # Get top and bottom edges
+    in_height, _ = torch.max(masks, dim=-1)
+    in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]
+    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
+    in_height_coords = in_height_coords + h * (~in_height)
+    top_edges, _ = torch.min(in_height_coords, dim=-1)
+
+    # Get left and right edges
+    in_width, _ = torch.max(masks, dim=-2)
+    in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]
+    right_edges, _ = torch.max(in_width_coords, dim=-1)
+    in_width_coords = in_width_coords + w * (~in_width)
+    left_edges, _ = torch.min(in_width_coords, dim=-1)
+
+    # If the mask is empty the right edge will be to the left of the left edge.
+    # Replace these boxes with [0, 0, 0, 0]
+    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
+    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
+    out = out * (~empty_filter).unsqueeze(-1)
+
+    # Return to original shape
+    if len(shape) > 2:
+        out = out.reshape(*shape[:-2], 4)
+    else:
+        out = out[0]
+
+    return out

tasks/kfc/wro-torchao-int8-rowwise-quant-loop16/task/environment/repo/torchao/_models/sam2/utils/misc.py

@@ -0,0 +1,382 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import warnings
+from threading import Thread
+from typing import Tuple, Union
+
+import numpy as np
+import torch
+from PIL import Image
+from tqdm import tqdm
+
+
+def get_sdpa_settings():
+    if torch.cuda.is_available():
+        old_gpu = torch.cuda.get_device_properties(0).major < 7
+        # only use Flash Attention on Ampere (8.0) or newer GPUs
+        use_flash_attn = torch.cuda.get_device_properties(0).major >= 8
+        if not use_flash_attn:
+            warnings.warn(
+                "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.",
+                category=UserWarning,
+                stacklevel=2,
+            )
+        # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only
+        # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases)
+        pytorch_version = tuple(int(v) for v in torch.__version__.split(".")[:2])
+        if pytorch_version < (2, 2):
+            warnings.warn(
+                f"You are using PyTorch {torch.__version__} without Flash Attention v2 support. "
+                "Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).",
+                category=UserWarning,
+                stacklevel=2,
+            )
+        math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn
+    else:
+        old_gpu = True
+        use_flash_attn = False
+        math_kernel_on = True
+
+    return old_gpu, use_flash_attn, math_kernel_on
+
+
+def get_connected_components(mask):
+    """
+    Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W).
+
+    Inputs:
+    - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is
+            background.
+
+    Outputs:
+    - labels: A tensor of shape (N, 1, H, W) containing the connected component labels
+              for foreground pixels and 0 for background pixels.
+    - counts: A tensor of shape (N, 1, H, W) containing the area of the connected
+              components for foreground pixels and 0 for background pixels.
+    """
+    from sam2 import _C
+
+    return _C.get_connected_componnets(mask.to(torch.uint8).contiguous())
+
+
+def mask_to_box(masks: torch.Tensor):
+    """
+    compute bounding box given an input mask
+
+    Inputs:
+    - masks: [B, 1, H, W] masks, dtype=torch.Tensor
+
+    Returns:
+    - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor
+    """
+    B, _, h, w = masks.shape
+    device = masks.device
+    xs = torch.arange(w, device=device, dtype=torch.int32)
+    ys = torch.arange(h, device=device, dtype=torch.int32)
+    grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing="xy")
+    grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)
+    grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)
+    min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)
+    max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)
+    min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)
+    max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)
+    bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)
+
+    return bbox_coords
+
+
+def _load_img_as_tensor(img_path, image_size):
+    img_pil = Image.open(img_path)
+    img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size)))
+    if img_np.dtype == np.uint8:  # np.uint8 is expected for JPEG images
+        img_np = img_np / 255.0
+    else:
+        raise RuntimeError(f"Unknown image dtype: {img_np.dtype} on {img_path}")
+    img = torch.from_numpy(img_np).permute(2, 0, 1)
+    video_width, video_height = img_pil.size  # the original video size
+    return img, video_height, video_width
+
+
+class AsyncVideoFrameLoader:
+    """
+    A list of video frames to be load asynchronously without blocking session start.
+    """
+
+    def __init__(
+        self,
+        img_paths,
+        image_size,
+        offload_video_to_cpu,
+        img_mean,
+        img_std,
+        compute_device,
+    ):
+        self.img_paths = img_paths
+        self.image_size = image_size
+        self.offload_video_to_cpu = offload_video_to_cpu
+        self.img_mean = img_mean
+        self.img_std = img_std
+        # items in `self.images` will be loaded asynchronously
+        self.images = [None] * len(img_paths)
+        # catch and raise any exceptions in the async loading thread
+        self.exception = None
+        # video_height and video_width be filled when loading the first image
+        self.video_height = None
+        self.video_width = None
+        self.compute_device = compute_device
+
+        # load the first frame to fill video_height and video_width and also
+        # to cache it (since it's most likely where the user will click)
+        self.__getitem__(0)
+
+        # load the rest of frames asynchronously without blocking the session start
+        def _load_frames():
+            try:
+                for n in tqdm(range(len(self.images)), desc="frame loading (JPEG)"):
+                    self.__getitem__(n)
+            except Exception as e:
+                self.exception = e
+
+        self.thread = Thread(target=_load_frames, daemon=True)
+        self.thread.start()
+
+    def __getitem__(self, index):
+        if self.exception is not None:
+            raise RuntimeError("Failure in frame loading thread") from self.exception
+
+        img = self.images[index]
+        if img is not None:
+            return img
+
+        img, video_height, video_width = _load_img_as_tensor(
+            self.img_paths[index], self.image_size
+        )
+        self.video_height = video_height
+        self.video_width = video_width
+        # normalize by mean and std
+        img -= self.img_mean
+        img /= self.img_std
+        if not self.offload_video_to_cpu:
+            img = img.to(self.compute_device, non_blocking=True)
+        self.images[index] = img
+        return img
+
+    def __len__(self):
+        return len(self.images)
+
+
+def load_video_frames(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.485, 0.456, 0.406),
+    img_std=(0.229, 0.224, 0.225),
+    async_loading_frames=False,
+    compute_device=torch.device("cuda"),
+):
+    """
+    Load the video frames from video_path. The frames are resized to image_size as in
+    the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.
+    """
+    is_bytes = isinstance(video_path, bytes)
+    is_str = isinstance(video_path, str)
+    is_mp4_path = is_str and os.path.splitext(video_path)[-1] in [".mp4", ".MP4"]
+    if is_bytes or is_mp4_path:
+        return load_video_frames_from_video_file(
+            video_path=video_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            compute_device=compute_device,
+        )
+    elif is_str and os.path.isdir(video_path):
+        return load_video_frames_from_jpg_images(
+            video_path=video_path,
+            image_size=image_size,
+            offload_video_to_cpu=offload_video_to_cpu,
+            img_mean=img_mean,
+            img_std=img_std,
+            async_loading_frames=async_loading_frames,
+            compute_device=compute_device,
+        )
+    else:
+        raise NotImplementedError(
+            "Only MP4 video and JPEG folder are supported at this moment"
+        )
+
+
+def load_video_frames_from_jpg_images(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.485, 0.456, 0.406),
+    img_std=(0.229, 0.224, 0.225),
+    async_loading_frames=False,
+    compute_device=torch.device("cuda"),
+):
+    """
+    Load the video frames from a directory of JPEG files ("<frame_index>.jpg" format).
+
+    The frames are resized to image_size x image_size and are loaded to GPU if
+    `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.
+
+    You can load a frame asynchronously by setting `async_loading_frames` to `True`.
+    """
+    if isinstance(video_path, str) and os.path.isdir(video_path):
+        jpg_folder = video_path
+    else:
+        raise NotImplementedError(
+            "Only JPEG frames are supported at this moment. For video files, you may use "
+            "ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \n"
+            "```\n"
+            "ffmpeg -i <your_video>.mp4 -q:v 2 -start_number 0 <output_dir>/'%05d.jpg'\n"
+            "```\n"
+            "where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks "
+            "ffmpeg to start the JPEG file from 00000.jpg."
+        )
+
+    frame_names = [
+        p
+        for p in os.listdir(jpg_folder)
+        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
+    ]
+    frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+    num_frames = len(frame_names)
+    if num_frames == 0:
+        raise RuntimeError(f"no images found in {jpg_folder}")
+    img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]
+    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+
+    if async_loading_frames:
+        lazy_images = AsyncVideoFrameLoader(
+            img_paths,
+            image_size,
+            offload_video_to_cpu,
+            img_mean,
+            img_std,
+            compute_device,
+        )
+        return lazy_images, lazy_images.video_height, lazy_images.video_width
+
+    images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)
+    for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")):
+        images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)
+    if not offload_video_to_cpu:
+        images = images.to(compute_device)
+        img_mean = img_mean.to(compute_device)
+        img_std = img_std.to(compute_device)
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, video_height, video_width
+
+
+def load_video_frames_from_video_file(
+    video_path,
+    image_size,
+    offload_video_to_cpu,
+    img_mean=(0.485, 0.456, 0.406),
+    img_std=(0.229, 0.224, 0.225),
+    compute_device=torch.device("cuda"),
+):
+    """Load the video frames from a video file."""
+    import decord
+
+    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
+    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]
+    # Get the original video height and width
+    decord.bridge.set_bridge("torch")
+    video_height, video_width, _ = decord.VideoReader(video_path).next().shape
+    # Iterate over all frames in the video
+    images = []
+    for frame in decord.VideoReader(video_path, width=image_size, height=image_size):
+        images.append(frame.permute(2, 0, 1))
+
+    images = torch.stack(images, dim=0).float() / 255.0
+    if not offload_video_to_cpu:
+        images = images.to(compute_device)
+        img_mean = img_mean.to(compute_device)
+        img_std = img_std.to(compute_device)
+    # normalize by mean and std
+    images -= img_mean
+    images /= img_std
+    return images, video_height, video_width
+
+
+def fill_holes_in_mask_scores(mask, max_area):
+    """
+    A post processor to fill small holes in mask scores with area under `max_area`.
+    """
+    # Holes are those connected components in background with area <= self.max_area
+    # (background regions are those with mask scores <= 0)
+    assert max_area > 0, "max_area must be positive"
+
+    input_mask = mask
+    try:
+        labels, areas = get_connected_components(mask <= 0)
+        is_hole = (labels > 0) & (areas <= max_area)
+        # We fill holes with a small positive mask score (0.1) to change them to foreground.
+        mask = torch.where(is_hole, 0.1, mask)
+    except Exception as e:
+        # Skip the post-processing step on removing small holes if the CUDA kernel fails
+        warnings.warn(
+            f"{e}\n\nSkipping the post-processing step due to the error above. You can "
+            "still use SAM 2 and it's OK to ignore the error above, although some post-processing "
+            "functionality may be limited (which doesn't affect the results in most cases; see "
+            "https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).",
+            category=UserWarning,
+            stacklevel=2,
+        )
+        mask = input_mask
+
+    return mask
+
+
+def concat_points(old_point_inputs, new_points, new_labels):
+    """Add new points and labels to previous point inputs (add at the end)."""
+    if old_point_inputs is None:
+        points, labels = new_points, new_labels
+    else:
+        points = torch.cat([old_point_inputs["point_coords"], new_points], dim=1)
+        labels = torch.cat([old_point_inputs["point_labels"], new_labels], dim=1)
+
+    return {"point_coords": points, "point_labels": labels}
+
+
+def get_image_size(image: Union[np.ndarray, torch.Tensor]):
+    if isinstance(image, np.ndarray):
+        return image.shape[:2]
+    elif isinstance(image, torch.Tensor):
+        _, h, w = image.shape
+        return (h, w)
+    elif isinstance(image, Image):
+        w, h = image.size
+        return (h, w)
+    else:
+        raise NotImplementedError(
+            f"Only support np.ndarray, torch.Tensoror PIL Image, but got {type(image)}"
+        )
+
+
+def crop_image(
+    image: Union[np.ndarray, torch.Tensor], crop_box: Tuple[int, int, int, int]
+):
+    x0, y0, x1, y1 = crop_box
+    if isinstance(image, np.ndarray):
+        # HxWxC
+        return image[y0:y1, x0:x1, :]
+    elif isinstance(image, torch.Tensor):
+        # CxHxW
+        return image[:, y0:y1, x0:x1]
+    else:
+        raise ValueError(
+            "Expected image to be of type np.ndarray or "
+            f"torch.Tensor, but got {type(image)}"
+        )