Update for nn.layers.distributed

flux/flux/model.py

@@ -109,10 +109,10 @@ class Flux(nn.Module):
             block.txt_attn.num_heads //= N
             block.sharding_group = group
             block.img_attn.qkv = shard_linear(
-                block.img_attn.qkv, "all-to-sharded", groups=3, group=group
+                block.img_attn.qkv, "all-to-sharded", segments=3, group=group
             )
             block.txt_attn.qkv = shard_linear(
-                block.txt_attn.qkv, "all-to-sharded", groups=3, group=group
+                block.txt_attn.qkv, "all-to-sharded", segments=3, group=group
             )
             shard_inplace(block.img_attn.proj, "sharded-to-all", group=group)
             shard_inplace(block.txt_attn.proj, "sharded-to-all", group=group)
@@ -131,11 +131,11 @@ class Flux(nn.Module):
             block.linear1 = shard_linear(
                 block.linear1,
                 "all-to-sharded",
-                groups=[1 / 7, 2 / 7, 3 / 7],
+                segments=[1 / 7, 2 / 7, 3 / 7],
                 group=group,
             )
             block.linear2 = shard_linear(
-                block.linear2, "sharded-to-all", groups=[1 / 5], group=group
+                block.linear2, "sharded-to-all", segments=[1 / 5], group=group
             )
 
     def __call__(

flux/txt2image.py

@@ -62,6 +62,7 @@ if __name__ == "__main__":
     parser.add_argument("--adapter")
     parser.add_argument("--fuse-adapter", action="store_true")
     parser.add_argument("--no-t5-padding", dest="t5_padding", action="store_false")
+    parser.add_argument("--force-shard", action="store_true")
     args = parser.parse_args()
 
     # Load the models
@@ -77,8 +78,16 @@ if __name__ == "__main__":
         nn.quantize(flux.clip, class_predicate=quantization_predicate)
 
     group = mx.distributed.init()
+    n_images = args.n_images
+    should_gather = False
     if group.size() > 1:
+        if args.force_shard or n_images < group.size() or n_images % group.size() != 0:
             flux.flow.shard(group)
+            if args.seed is None:
+                args.seed = mx.distributed.all_sum(mx.random.randint(0, 2**20)).item()
+        else:
+            n_images //= group.size()
+            should_gather = True
 
     if args.preload_models:
         flux.ensure_models_are_loaded()
@@ -87,7 +96,7 @@ if __name__ == "__main__":
     latent_size = to_latent_size(args.image_size)
     latents = flux.generate_latents(
         args.prompt,
-        n_images=args.n_images,
+        n_images=n_images,
         num_steps=args.steps,
         latent_size=latent_size,
         guidance=args.guidance,
@@ -97,8 +106,8 @@ if __name__ == "__main__":
     # First we get and eval the conditioning
     conditioning = next(latents)
     mx.eval(conditioning)
-    peak_mem_conditioning = mx.metal.get_peak_memory() / 1024**3
+    peak_mem_conditioning = mx.get_peak_memory() / 1024**3
-    mx.metal.reset_peak_memory()
+    mx.reset_peak_memory()
 
     # The following is not necessary but it may help in memory constrained
     # systems by reusing the memory kept by the text encoders.
@@ -106,36 +115,42 @@ if __name__ == "__main__":
     del flux.clip
 
     # Actual denoising loop
-    for x_t in tqdm(latents, total=args.steps):
+    for x_t in tqdm(latents, total=args.steps, disable=group.rank() > 0):
         mx.eval(x_t)
 
     # The following is not necessary but it may help in memory constrained
     # systems by reusing the memory kept by the flow transformer.
     del flux.flow
-    peak_mem_generation = mx.metal.get_peak_memory() / 1024**3
+    peak_mem_generation = mx.get_peak_memory() / 1024**3
-    mx.metal.reset_peak_memory()
+    mx.reset_peak_memory()
 
     # Decode them into images
     decoded = []
     for i in tqdm(range(0, args.n_images, args.decoding_batch_size)):
         decoded.append(flux.decode(x_t[i : i + args.decoding_batch_size], latent_size))
         mx.eval(decoded[-1])
-    peak_mem_decoding = mx.metal.get_peak_memory() / 1024**3
+    peak_mem_decoding = mx.get_peak_memory() / 1024**3
     peak_mem_overall = max(
         peak_mem_conditioning, peak_mem_generation, peak_mem_decoding
     )
 
+    # Gather them if each node has different images
+    decoded = mx.concatenate(decoded, axis=0)
+    if should_gather:
+        decoded = mx.distributed.all_gather(decoded)
+        mx.eval(decoded)
+
     if args.save_raw:
         *name, suffix = args.output.split(".")
         name = ".".join(name)
-        x = mx.concatenate(decoded, axis=0)
+        x = decoded
         x = (x * 255).astype(mx.uint8)
         for i in range(len(x)):
             im = Image.fromarray(np.array(x[i]))
             im.save(".".join([name, str(i), suffix]))
     else:
         # Arrange them on a grid
-        x = mx.concatenate(decoded, axis=0)
+        x = decoded
         x = mx.pad(x, [(0, 0), (4, 4), (4, 4), (0, 0)])
         B, H, W, C = x.shape
         x = x.reshape(args.n_rows, B // args.n_rows, H, W, C).transpose(0, 2, 1, 3, 4)