beat mlx at resnet 18 (#6611)

* work to beat mlx at resnet18 [run_process_replay]

* pruning

* wino sometimes

* shorter

* comment

extra/resnet18/resnet_mlx.py

@@ -0,0 +1,114 @@
+# code from https://x.com/awnihannun/status/1832511021602500796
+from huggingface_hub import snapshot_download
+import mlx.core as mx
+import mlx.nn as nn
+import time
+
+
+class Block(nn.Module):
+  def __init__(self, in_dims, dims, stride=1):
+    super().__init__()
+
+    self.conv1 = nn.Conv2d(
+      in_dims, dims, kernel_size=3, stride=stride, padding=1, bias=False
+    )
+    self.bn1 = nn.BatchNorm(dims)
+
+    self.conv2 = nn.Conv2d(
+      dims, dims, kernel_size=3, stride=1, padding=1, bias=False
+    )
+    self.bn2 = nn.BatchNorm(dims)
+
+    self.downsample = []
+    if stride != 1:
+      self.downsample = [
+        nn.Conv2d(in_dims, dims, kernel_size=1, stride=stride, bias=False),
+        nn.BatchNorm(dims)
+      ]
+
+  def __call__(self, x):
+    out = nn.relu(self.bn1(self.conv1(x)))
+    out = self.bn2(self.conv2(out))
+    for l in self.downsample:
+      x = l(x)
+    out += x
+    out = nn.relu(out)
+    return out
+
+
+class ResNet(nn.Module):
+  def __init__(self, block, num_blocks, num_classes=10):
+    super().__init__()
+    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+    self.bn1 = nn.BatchNorm(64)
+
+    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+    self.layer1 = self._make_layer(block, 64, 64, num_blocks[0], stride=1)
+    self.layer2 = self._make_layer(block, 64, 128, num_blocks[1], stride=2)
+    self.layer3 = self._make_layer(block, 128, 256, num_blocks[2], stride=2)
+    self.layer4 = self._make_layer(block, 256, 512, num_blocks[3], stride=2)
+
+    self.fc = nn.Linear(512, num_classes)
+
+  def _make_layer(self, block, in_dims, dims, num_blocks, stride):
+    strides = [stride] + [1] * (num_blocks - 1)
+    layers = []
+    for stride in strides:
+      layers.append(block(in_dims, dims, stride))
+      in_dims = dims
+    return layers
+
+  def __call__(self, x):
+    x = nn.relu(self.bn1(self.conv1(x)))
+    x = self.maxpool(x)
+    for l in self.layer1 + self.layer2 + self.layer3 + self.layer4:
+      x = l(x)
+    x = mx.mean(x, axis=[1, 2])
+    x = self.fc(x)
+    return x
+
+
+
+def load():
+  model = ResNet(Block, [2, 2, 2, 2], num_classes=1000)
+  file = "model.safetensors"
+  model_path = snapshot_download(
+    repo_id="awni/resnet18-mlx",
+    allow_patterns=[file],
+  )
+  model.load_weights(model_path + "/" + file)
+  model.eval()
+  mx.eval(model)
+  return model
+
+if __name__ == "__main__":
+
+  resnet18 = load()
+
+  @mx.compile
+  def forward(im):
+    return resnet18(im)
+
+  batch_sizes = [1, 2, 4, 8, 16, 32, 64]
+  #its = 200
+  #batch_sizes = [64]
+  its = 20
+  print(f"Batch Size | Images-per-second | Milliseconds-per-image")
+  print(f"---- | ---- | ---- ")
+  for N in batch_sizes:
+    image = mx.random.uniform(shape=(N, 288, 288, 3))
+
+    # Warmup
+    for _ in range(5):
+      output = forward(image)
+      mx.eval(output)
+
+    tic = time.time()
+    for _ in range(its):
+      output = forward(image)
+      mx.async_eval(output)
+    mx.eval(output)
+    toc = time.time()
+    ims_per_sec = N * its / (toc - tic)
+    ms_per_im = 1e3 / ims_per_sec
+    print(f"{N} | {ims_per_sec:.3f} | {ms_per_im:.3f}")

extra/resnet18/resnet_tinygrad.py

@@ -0,0 +1,110 @@
+from huggingface_hub import snapshot_download
+from tinygrad import nn, Tensor, TinyJit, Device, GlobalCounters, Context
+import time
+
+class Block:
+  def __init__(self, in_dims, dims, stride=1):
+    super().__init__()
+
+    self.conv1 = nn.Conv2d(
+      in_dims, dims, kernel_size=3, stride=stride, padding=1, bias=False
+    )
+    self.bn1 = nn.BatchNorm(dims)
+
+    self.conv2 = nn.Conv2d(
+      dims, dims, kernel_size=3, stride=1, padding=1, bias=False
+    )
+    self.bn2 = nn.BatchNorm(dims)
+
+    self.downsample = []
+    if stride != 1:
+      self.downsample = [
+        nn.Conv2d(in_dims, dims, kernel_size=1, stride=stride, bias=False),
+        nn.BatchNorm(dims)
+      ]
+
+  def __call__(self, x):
+    out = self.bn1(self.conv1(x)).relu()
+    out = self.bn2(self.conv2(out))
+    for l in self.downsample:
+      x = l(x)
+    out += x
+    return out.relu()
+
+
+class ResNet:
+  def __init__(self, block, num_blocks, num_classes=10):
+    super().__init__()
+    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+    self.bn1 = nn.BatchNorm(64)
+
+    self.layer1 = self._make_layer(block, 64, 64, num_blocks[0], stride=1)
+    self.layer2 = self._make_layer(block, 64, 128, num_blocks[1], stride=2)
+    self.layer3 = self._make_layer(block, 128, 256, num_blocks[2], stride=2)
+    self.layer4 = self._make_layer(block, 256, 512, num_blocks[3], stride=2)
+
+    self.fc = nn.Linear(512, num_classes)
+
+  def _make_layer(self, block, in_dims, dims, num_blocks, stride):
+    strides = [stride] + [1] * (num_blocks - 1)
+    layers = []
+    for stride in strides:
+      layers.append(block(in_dims, dims, stride))
+      in_dims = dims
+    return layers
+
+  def __call__(self, x:Tensor):
+    x = self.bn1(self.conv1(x)).relu().max_pool2d()
+    x = x.sequential(self.layer1)
+    with Context(WINO=1): x = x.sequential(self.layer2 + self.layer3 + self.layer4)
+    x = x.mean([2, 3])
+    x = self.fc(x)
+    return x
+
+
+
+def load():
+  model = ResNet(Block, [2, 2, 2, 2], num_classes=1000)
+  file = "model.safetensors"
+  model_path = snapshot_download(
+    repo_id="awni/resnet18-mlx",
+    allow_patterns=[file],
+  )
+  state = nn.state.safe_load(model_path + "/" + file)
+  # mlx is NHWC, tinygrad is NCHW
+  nn.state.load_state_dict(model, {k:v if len(v.shape) != 4 else v.to(None).permute(0,3,1,2).contiguous() for k,v in state.items()}, strict=False)
+  return model
+
+if __name__ == "__main__":
+
+  resnet18 = load()
+
+  @Tensor.test()
+  def _forward(im): return resnet18(im)
+  forward = TinyJit(_forward, prune=True)
+
+  batch_sizes = [1, 2, 4, 8, 16, 32, 64]
+  #its = 200
+  #batch_sizes = [64]
+  its = 20
+  print(f"Batch Size | Images-per-second | Milliseconds-per-image")
+  print(f"---- | ---- | ---- ")
+  for N in batch_sizes:
+    forward.reset()   # reset the JIT for a new batch size (could make automatic)
+    image = Tensor.uniform(N, 3, 288, 288)
+
+    # Warmup
+    for _ in range(5):
+      GlobalCounters.reset()
+      output = forward(image)
+    Device.default.synchronize()
+
+    tic = time.time()
+    for _ in range(its):
+      GlobalCounters.reset()
+      output = forward(image)
+    Device.default.synchronize()
+    toc = time.time()
+    ims_per_sec = N * its / (toc - tic)
+    ms_per_im = 1e3 / ims_per_sec
+    print(f"{N} | {ims_per_sec:.3f} | {ms_per_im:.3f}")