fix to work T5XXL with fp16

library/sd3_models.py

@@ -1124,7 +1124,12 @@ class Upsample(torch.nn.Module):
         self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
 
     def forward(self, x):
+        org_dtype = x.dtype
+        if x.dtype == torch.bfloat16:
+            x = x.to(torch.float32)
         x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        if x.dtype != org_dtype:
+            x = x.to(org_dtype)
         x = self.conv(x)
         return x
 
@@ -1263,11 +1268,11 @@ class SDVAE(torch.nn.Module):
     def dtype(self):
         return next(self.parameters()).dtype
 
-    @torch.autocast("cuda", dtype=torch.float16)
+    # @torch.autocast("cuda", dtype=torch.float16)
     def decode(self, latent):
         return self.decoder(latent)
 
-    @torch.autocast("cuda", dtype=torch.float16)
+    # @torch.autocast("cuda", dtype=torch.float16)
     def encode(self, image):
         hidden = self.encoder(image)
         mean, logvar = torch.chunk(hidden, 2, dim=1)
@@ -1630,10 +1635,25 @@ class T5LayerNorm(torch.nn.Module):
         self.weight = torch.nn.Parameter(torch.ones(hidden_size, dtype=dtype, device=device))
         self.variance_epsilon = eps
 
-    def forward(self, x):
-        variance = x.pow(2).mean(-1, keepdim=True)
-        x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
+    # def forward(self, x):
+    #     variance = x.pow(2).mean(-1, keepdim=True)
+    #     x = x * torch.rsqrt(variance + self.variance_epsilon)
+    #     return self.weight.to(device=x.device, dtype=x.dtype) * x
+
+    # copy from transformers' T5LayerNorm
+    def forward(self, hidden_states):
+        # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+        # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
+        # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
+        # half-precision inputs is done in fp32
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        # convert into half-precision if necessary
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
 
 
 class T5DenseGatedActDense(torch.nn.Module):
@@ -1775,7 +1795,27 @@ class T5Block(torch.nn.Module):
 
     def forward(self, x, past_bias=None):
         x, past_bias = self.layer[0](x, past_bias)
+
+        # copy from transformers' T5Block
+        # clamp inf values to enable fp16 training
+        if x.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(x).any(),
+                torch.finfo(x.dtype).max - 1000,
+                torch.finfo(x.dtype).max,
+            )
+            x = torch.clamp(x, min=-clamp_value, max=clamp_value)
+
         x = self.layer[-1](x)
+        # clamp inf values to enable fp16 training
+        if x.dtype == torch.float16:
+            clamp_value = torch.where(
+                torch.isinf(x).any(),
+                torch.finfo(x.dtype).max - 1000,
+                torch.finfo(x.dtype).max,
+            )
+            x = torch.clamp(x, min=-clamp_value, max=clamp_value)
+
         return x, past_bias
 
 
@@ -1896,4 +1936,96 @@ def create_t5xxl(device="cpu", dtype=torch.float32, state_dict: Optional[Dict[st
     return t5
 
 
+"""
+    # snippet for using the T5 model from transformers
+    
+    from transformers import T5EncoderModel, T5Config
+    import accelerate
+    import json
+
+    T5_CONFIG_JSON = ""
+{
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}
+""
+    config = json.loads(T5_CONFIG_JSON)
+    config = T5Config(**config)
+
+    # model = T5EncoderModel.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="text_encoder_3")
+    # print(model.config)
+    # # model(**load_model.config)
+
+    # with accelerate.init_empty_weights():
+    model = T5EncoderModel._from_config(config)  # , torch_dtype=dtype)
+    for key in list(state_dict.keys()):
+        if key.startswith("transformer."):
+            new_key = key[len("transformer.") :]
+            state_dict[new_key] = state_dict.pop(key)
+
+    info = model.load_state_dict(state_dict)
+    print(info)
+    model.set_attn_mode = lambda x: None
+    # model.to("cpu")
+
+    _self = model
+
+    def enc(list_of_token_weight_pairs):
+        has_batch = isinstance(list_of_token_weight_pairs[0][0], list)
+
+        if has_batch:
+            list_of_tokens = []
+            for pairs in list_of_token_weight_pairs:
+                tokens = [a[0] for a in pairs[0]]  # I'm not sure why this is [0]
+                list_of_tokens.append(tokens)
+        else:
+            list_of_tokens = [[a[0] for a in list_of_token_weight_pairs[0]]]
+
+        list_of_tokens = np.array(list_of_tokens)
+        list_of_tokens = torch.from_numpy(list_of_tokens).to("cuda", dtype=torch.long)
+        out = _self(list_of_tokens)
+        pooled = None
+        if has_batch:
+            return out, pooled
+        else:
+            if pooled is not None:
+                first_pooled = pooled[0:1]
+            else:
+                first_pooled = pooled
+            return out[0], first_pooled
+            # output = [out[0:1]]
+            # return torch.cat(output, dim=-2), first_pooled
+
+    model.encode_token_weights = enc
+
+    return model
+"""
+
 # endregion