Rerange experimental

exps/experimental/GeMOSA/meta_model_ablation.py

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+#
+# This is used for the ablation studies:
+# The meta-model in this file uses the traditional attention in
+# transformer.
+#
+import torch
+
+import torch.nn.functional as F
+
+from xautodl.xlayers import super_core
+from xautodl.xlayers import trunc_normal_
+from xautodl.models.xcore import get_model
+
+
+class MetaModel_TraditionalAtt(super_core.SuperModule):
+    """Learning to Generate Models One Step Ahead (Meta Model Design)."""
+
+    def __init__(
+        self,
+        shape_container,
+        layer_dim,
+        time_dim,
+        meta_timestamps,
+        dropout: float = 0.1,
+        seq_length: int = None,
+        interval: float = None,
+        thresh: float = None,
+    ):
+        super(MetaModel_TraditionalAtt, self).__init__()
+        self._shape_container = shape_container
+        self._num_layers = len(shape_container)
+        self._numel_per_layer = []
+        for ilayer in range(self._num_layers):
+            self._numel_per_layer.append(shape_container[ilayer].numel())
+        self._raw_meta_timestamps = meta_timestamps
+        assert interval is not None
+        self._interval = interval
+        self._thresh = interval * seq_length if thresh is None else thresh
+
+        self.register_parameter(
+            "_super_layer_embed",
+            torch.nn.Parameter(torch.Tensor(self._num_layers, layer_dim)),
+        )
+        self.register_parameter(
+            "_super_meta_embed",
+            torch.nn.Parameter(torch.Tensor(len(meta_timestamps), time_dim)),
+        )
+        self.register_buffer("_meta_timestamps", torch.Tensor(meta_timestamps))
+        self._time_embed_dim = time_dim
+        self._append_meta_embed = dict(fixed=None, learnt=None)
+        self._append_meta_timestamps = dict(fixed=None, learnt=None)
+
+        self._tscalar_embed = super_core.SuperDynamicPositionE(
+            time_dim, scale=1 / interval
+        )
+
+        # build transformer
+        self._trans_att = super_core.SuperQKVAttention(
+            in_q_dim=time_dim,
+            in_k_dim=time_dim,
+            in_v_dim=time_dim,
+            num_heads=4,
+            proj_dim=time_dim,
+            qkv_bias=True,
+            attn_drop=None,
+            proj_drop=dropout,
+        )
+
+        model_kwargs = dict(
+            config=dict(model_type="dual_norm_mlp"),
+            input_dim=layer_dim + time_dim,
+            output_dim=max(self._numel_per_layer),
+            hidden_dims=[(layer_dim + time_dim) * 2] * 3,
+            act_cls="gelu",
+            norm_cls="layer_norm_1d",
+            dropout=dropout,
+        )
+        self._generator = get_model(**model_kwargs)
+
+        # initialization
+        trunc_normal_(
+            [self._super_layer_embed, self._super_meta_embed],
+            std=0.02,
+        )
+
+    def get_parameters(self, time_embed, attention, generator):
+        parameters = []
+        if time_embed:
+            parameters.append(self._super_meta_embed)
+        if attention:
+            parameters.extend(list(self._trans_att.parameters()))
+        if generator:
+            parameters.append(self._super_layer_embed)
+            parameters.extend(list(self._generator.parameters()))
+        return parameters
+
+    @property
+    def meta_timestamps(self):
+        with torch.no_grad():
+            meta_timestamps = [self._meta_timestamps]
+            for key in ("fixed", "learnt"):
+                if self._append_meta_timestamps[key] is not None:
+                    meta_timestamps.append(self._append_meta_timestamps[key])
+        return torch.cat(meta_timestamps)
+
+    @property
+    def super_meta_embed(self):
+        meta_embed = [self._super_meta_embed]
+        for key in ("fixed", "learnt"):
+            if self._append_meta_embed[key] is not None:
+                meta_embed.append(self._append_meta_embed[key])
+        return torch.cat(meta_embed)
+
+    def create_meta_embed(self):
+        param = torch.Tensor(1, self._time_embed_dim)
+        trunc_normal_(param, std=0.02)
+        param = param.to(self._super_meta_embed.device)
+        param = torch.nn.Parameter(param, True)
+        return param
+
+    def get_closest_meta_distance(self, timestamp):
+        with torch.no_grad():
+            distances = torch.abs(self.meta_timestamps - timestamp)
+            return torch.min(distances).item()
+
+    def replace_append_learnt(self, timestamp, meta_embed):
+        self._append_meta_timestamps["learnt"] = timestamp
+        self._append_meta_embed["learnt"] = meta_embed
+
+    @property
+    def meta_length(self):
+        return self.meta_timestamps.numel()
+
+    def clear_fixed(self):
+        self._append_meta_timestamps["fixed"] = None
+        self._append_meta_embed["fixed"] = None
+
+    def clear_learnt(self):
+        self.replace_append_learnt(None, None)
+
+    def append_fixed(self, timestamp, meta_embed):
+        with torch.no_grad():
+            device = self._super_meta_embed.device
+            timestamp = timestamp.detach().clone().to(device)
+            meta_embed = meta_embed.detach().clone().to(device)
+            if self._append_meta_timestamps["fixed"] is None:
+                self._append_meta_timestamps["fixed"] = timestamp
+            else:
+                self._append_meta_timestamps["fixed"] = torch.cat(
+                    (self._append_meta_timestamps["fixed"], timestamp), dim=0
+                )
+            if self._append_meta_embed["fixed"] is None:
+                self._append_meta_embed["fixed"] = meta_embed
+            else:
+                self._append_meta_embed["fixed"] = torch.cat(
+                    (self._append_meta_embed["fixed"], meta_embed), dim=0
+                )
+
+    def gen_time_embed(self, timestamps):
+        # timestamps is a batch of timestamps
+        [B] = timestamps.shape
+        # batch, seq = timestamps.shape
+        timestamps = timestamps.view(-1, 1)
+        meta_timestamps, meta_embeds = self.meta_timestamps, self.super_meta_embed
+        timestamp_v_embed = meta_embeds.unsqueeze(dim=0)
+        timestamp_q_embed = self._tscalar_embed(timestamps)
+        timestamp_k_embed = self._tscalar_embed(meta_timestamps.view(1, -1))
+
+        # create the mask
+        mask = (
+            torch.unsqueeze(timestamps, dim=-1) <= meta_timestamps.view(1, 1, -1)
+        ) | (
+            torch.abs(
+                torch.unsqueeze(timestamps, dim=-1) - meta_timestamps.view(1, 1, -1)
+            )
+            > self._thresh
+        )
+        timestamp_embeds = self._trans_att(
+            timestamp_q_embed, timestamp_k_embed, timestamp_v_embed, mask
+        )
+        return timestamp_embeds[:, -1, :]
+
+    def gen_model(self, time_embeds):
+        B, _ = time_embeds.shape
+        # create joint embed
+        num_layer, _ = self._super_layer_embed.shape
+        # The shape of `joint_embed` is batch * num-layers * input-dim
+        joint_embeds = torch.cat(
+            (
+                time_embeds.view(B, 1, -1).expand(-1, num_layer, -1),
+                self._super_layer_embed.view(1, num_layer, -1).expand(B, -1, -1),
+            ),
+            dim=-1,
+        )
+        batch_weights = self._generator(joint_embeds)
+        batch_containers = []
+        for weights in torch.split(batch_weights, 1):
+            batch_containers.append(
+                self._shape_container.translate(torch.split(weights.squeeze(0), 1))
+            )
+        return batch_containers
+
+    def forward_raw(self, timestamps, time_embeds, tembed_only=False):
+        raise NotImplementedError
+
+    def forward_candidate(self, input):
+        raise NotImplementedError
+
+    def easy_adapt(self, timestamp, time_embed):
+        with torch.no_grad():
+            timestamp = torch.Tensor([timestamp]).to(self._meta_timestamps.device)
+            self.replace_append_learnt(None, None)
+            self.append_fixed(timestamp, time_embed)
+
+    def adapt(self, base_model, criterion, timestamp, x, y, lr, epochs, init_info):
+        distance = self.get_closest_meta_distance(timestamp)
+        if distance + self._interval * 1e-2 <= self._interval:
+            return False, None
+        x, y = x.to(self._meta_timestamps.device), y.to(self._meta_timestamps.device)
+        with torch.set_grad_enabled(True):
+            new_param = self.create_meta_embed()
+
+            optimizer = torch.optim.Adam(
+                [new_param], lr=lr, weight_decay=1e-5, amsgrad=True
+            )
+            timestamp = torch.Tensor([timestamp]).to(new_param.device)
+            self.replace_append_learnt(timestamp, new_param)
+            self.train()
+            base_model.train()
+            if init_info is not None:
+                best_loss = init_info["loss"]
+                new_param.data.copy_(init_info["param"].data)
+            else:
+                best_loss = 1e9
+            with torch.no_grad():
+                best_new_param = new_param.detach().clone()
+            for iepoch in range(epochs):
+                optimizer.zero_grad()
+                time_embed = self.gen_time_embed(timestamp.view(1))
+                match_loss = F.l1_loss(new_param, time_embed)
+
+                [container] = self.gen_model(new_param.view(1, -1))
+                y_hat = base_model.forward_with_container(x, container)
+                meta_loss = criterion(y_hat, y)
+                loss = meta_loss + match_loss
+                loss.backward()
+                optimizer.step()
+                if meta_loss.item() < best_loss:
+                    with torch.no_grad():
+                        best_loss = meta_loss.item()
+                        best_new_param = new_param.detach().clone()
+        self.easy_adapt(timestamp, best_new_param)
+        return True, best_loss
+
+    def extra_repr(self) -> str:
+        return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(
+            list(self._super_layer_embed.shape),
+            list(self._super_meta_embed.shape),
+            list(self._meta_timestamps.shape),
+        )