Add some more transformer hooks and move tomesd to comfy_extras.

Tomesd now uses q instead of x to decide which tokens to merge because
it seems to give better results.

comfy_extras/nodes_tomesd.py

@@ -0,0 +1,177 @@
+#Taken from: https://github.com/dbolya/tomesd
+
+import torch
+from typing import Tuple, Callable
+import math
+
+def do_nothing(x: torch.Tensor, mode:str=None):
+    return x
+
+
+def mps_gather_workaround(input, dim, index):
+    if input.shape[-1] == 1:
+        return torch.gather(
+            input.unsqueeze(-1),
+            dim - 1 if dim < 0 else dim,
+            index.unsqueeze(-1)
+        ).squeeze(-1)
+    else:
+        return torch.gather(input, dim, index)
+
+
+def bipartite_soft_matching_random2d(metric: torch.Tensor,
+                                     w: int, h: int, sx: int, sy: int, r: int,
+                                     no_rand: bool = False) -> Tuple[Callable, Callable]:
+    """
+    Partitions the tokens into src and dst and merges r tokens from src to dst.
+    Dst tokens are partitioned by choosing one randomy in each (sx, sy) region.
+    Args:
+     - metric [B, N, C]: metric to use for similarity
+     - w: image width in tokens
+     - h: image height in tokens
+     - sx: stride in the x dimension for dst, must divide w
+     - sy: stride in the y dimension for dst, must divide h
+     - r: number of tokens to remove (by merging)
+     - no_rand: if true, disable randomness (use top left corner only)
+    """
+    B, N, _ = metric.shape
+
+    if r <= 0 or w == 1 or h == 1:
+        return do_nothing, do_nothing
+
+    gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather
+    
+    with torch.no_grad():
+        
+        hsy, wsx = h // sy, w // sx
+
+        # For each sy by sx kernel, randomly assign one token to be dst and the rest src
+        if no_rand:
+            rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64)
+        else:
+            rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device)
+        
+        # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead
+        idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64)
+        idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype))
+        idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx)
+
+        # Image is not divisible by sx or sy so we need to move it into a new buffer
+        if (hsy * sy) < h or (wsx * sx) < w:
+            idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64)
+            idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view
+        else:
+            idx_buffer = idx_buffer_view
+
+        # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices
+        rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1)
+
+        # We're finished with these
+        del idx_buffer, idx_buffer_view
+
+        # rand_idx is currently dst|src, so split them
+        num_dst = hsy * wsx
+        a_idx = rand_idx[:, num_dst:, :] # src
+        b_idx = rand_idx[:, :num_dst, :] # dst
+
+        def split(x):
+            C = x.shape[-1]
+            src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C))
+            dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C))
+            return src, dst
+
+        # Cosine similarity between A and B
+        metric = metric / metric.norm(dim=-1, keepdim=True)
+        a, b = split(metric)
+        scores = a @ b.transpose(-1, -2)
+
+        # Can't reduce more than the # tokens in src
+        r = min(a.shape[1], r)
+
+        # Find the most similar greedily
+        node_max, node_idx = scores.max(dim=-1)
+        edge_idx = node_max.argsort(dim=-1, descending=True)[..., None]
+
+        unm_idx = edge_idx[..., r:, :]  # Unmerged Tokens
+        src_idx = edge_idx[..., :r, :]  # Merged Tokens
+        dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx)
+
+    def merge(x: torch.Tensor, mode="mean") -> torch.Tensor:
+        src, dst = split(x)
+        n, t1, c = src.shape
+        
+        unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c))
+        src = gather(src, dim=-2, index=src_idx.expand(n, r, c))
+        dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode)
+
+        return torch.cat([unm, dst], dim=1)
+
+    def unmerge(x: torch.Tensor) -> torch.Tensor:
+        unm_len = unm_idx.shape[1]
+        unm, dst = x[..., :unm_len, :], x[..., unm_len:, :]
+        _, _, c = unm.shape
+
+        src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c))
+
+        # Combine back to the original shape
+        out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype)
+        out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst)
+        out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm)
+        out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src)
+
+        return out
+
+    return merge, unmerge
+
+
+def get_functions(x, ratio, original_shape):
+    b, c, original_h, original_w = original_shape
+    original_tokens = original_h * original_w
+    downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1])))
+    stride_x = 2
+    stride_y = 2
+    max_downsample = 1
+
+    if downsample <= max_downsample:
+        w = int(math.ceil(original_w / downsample))
+        h = int(math.ceil(original_h / downsample))
+        r = int(x.shape[1] * ratio)
+        no_rand = False
+        m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand)
+        return m, u
+
+    nothing = lambda y: y
+    return nothing, nothing
+
+
+
+class TomePatchModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, ratio):
+        self.u = None
+        def tomesd_m(q, k, v, extra_options):
+            #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q
+            #however from my basic testing it seems that using q instead gives better results
+            m, self.u = get_functions(q, ratio, extra_options["original_shape"])
+            return m(q), k, v
+        def tomesd_u(n, extra_options):
+            return self.u(n)
+
+        m = model.clone()
+        m.set_model_attn1_patch(tomesd_m)
+        m.set_model_attn1_output_patch(tomesd_u)
+        return (m, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TomePatchModel": TomePatchModel,
+}