Fix BLIP2 mixed precision on CPU (#179)

* allow both cpu string and torch.device to be identified for model loading.

* blip2 amp cpu compatibility.

* use dtype=float16 by default.

lavis/models/__init__.py

@@ -6,6 +6,7 @@
 """
 
 import logging
+import torch
 from omegaconf import OmegaConf
 from lavis.common.registry import registry
 
@@ -211,7 +212,7 @@ def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"):
             """
         )
 
-    if device == "cpu":
+    if device == "cpu" or device == torch.device("cpu"):
         model = model.float()
 
     return model.to(device), vis_processors, txt_processors

lavis/models/blip2_models/blip2.py

@@ -4,6 +4,7 @@
  SPDX-License-Identifier: BSD-3-Clause
  For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 """
+import contextlib
 import logging
 import os
 import time
@@ -32,6 +33,16 @@ def init_tokenizer(cls):
         tokenizer.add_special_tokens({"bos_token": "[DEC]"})
         return tokenizer
 
+    def maybe_autocast(self, dtype=torch.float16):
+        # if on cpu, don't use autocast
+        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+        enable_autocast = self.device != torch.device("cpu")
+
+        if enable_autocast:
+            return torch.cuda.amp.autocast(dtype=dtype)
+        else:
+            return contextlib.nullcontext()
+
     @classmethod
     def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
         encoder_config = BertConfig.from_pretrained("bert-base-uncased")
@@ -42,7 +53,7 @@ def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
         encoder_config.query_length = num_query_token
         Qformer = BertLMHeadModel.from_pretrained(
             "bert-base-uncased", config=encoder_config
-        )                 
+        )
         query_tokens = nn.Parameter(
             torch.zeros(1, num_query_token, encoder_config.hidden_size)
         )
@@ -52,16 +63,17 @@ def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
     @classmethod
     def init_vision_encoder(
         cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
-    ):  
-        assert model_name in ["eva_clip_g","clip_L"], "vit model must be eva_clip_g or clip_L" 
-        if model_name=="eva_clip_g":
+    ):
+        assert model_name in [
+            "eva_clip_g",
+            "clip_L",
+        ], "vit model must be eva_clip_g or clip_L"
+        if model_name == "eva_clip_g":
             visual_encoder = create_eva_vit_g(
                 img_size, drop_path_rate, use_grad_checkpoint, precision
             )
-        elif model_name=="clip_L":
-             visual_encoder = create_clip_vit_L(
-                img_size, use_grad_checkpoint, precision
-            )               
+        elif model_name == "clip_L":
+            visual_encoder = create_clip_vit_L(img_size, use_grad_checkpoint, precision)
         ln_vision = LayerNorm(visual_encoder.num_features)
         return visual_encoder, ln_vision
 
@@ -80,7 +92,7 @@ def load_from_pretrained(self, url_or_filename):
 
         msg = self.load_state_dict(state_dict, strict=False)
 
-        logging.info("Missing keys {}".format(msg.missing_keys))
+        # logging.info("Missing keys {}".format(msg.missing_keys))
         logging.info("load checkpoint from %s" % url_or_filename)
 
         return msg

lavis/models/blip2_models/blip2_image_text_matching.py

@@ -54,9 +54,9 @@ def forward(self, samples, match_head="itm"):
         image = samples["image"]
         caption = samples["text_input"]
 
-        with torch.cuda.amp.autocast(enabled=(self.device != torch.device("cpu"))):
+        with self.maybe_autocast():
             image_embeds = self.ln_vision(self.visual_encoder(image))
-        image_embeds = image_embeds.float()    
+        image_embeds = image_embeds.float()
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
             image.device
         )

lavis/models/blip2_models/blip2_opt.py

@@ -59,7 +59,7 @@ def __init__(
         )
         if freeze_vit:
             for name, param in self.visual_encoder.named_parameters():
-                param.requires_grad = False               
+                param.requires_grad = False
             self.visual_encoder = self.visual_encoder.eval()
             self.visual_encoder.train = disabled_train
             logging.info("freeze vision encoder")
@@ -95,7 +95,8 @@ def __init__(
 
     def forward(self, samples):
         image = samples["image"]
-        image_embeds = self.ln_vision(self.visual_encoder(image))
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
             image.device
         )
@@ -138,12 +139,13 @@ def forward(self, samples):
         inputs_embeds = torch.cat([inputs_opt, inputs_embeds], dim=1)
         attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1)
 
-        outputs = self.opt_model(
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            return_dict=True,
-            labels=targets,
-        )
+        with self.maybe_autocast():
+            outputs = self.opt_model(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                return_dict=True,
+                labels=targets,
+            )
         loss = outputs.loss
 
         return {"loss": loss}
@@ -177,9 +179,7 @@ def generate(
             captions (list): A list of strings of length batch_size * num_captions.
         """
         image = samples["image"]
-        with torch.cuda.amp.autocast(
-            enabled=(self.device != torch.device("cpu"))
-        ):          
+        with self.maybe_autocast():
             image_embeds = self.ln_vision(self.visual_encoder(image))
             image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
                 image.device
@@ -194,7 +194,9 @@ def generate(
             )
 
             inputs_opt = self.opt_proj(query_output.last_hidden_state)
-            atts_opt = torch.ones(inputs_opt.size()[:-1], dtype=torch.long).to(image.device)
+            atts_opt = torch.ones(inputs_opt.size()[:-1], dtype=torch.long).to(
+                image.device
+            )
 
             if "prompt" in samples.keys():
                 prompt = samples["prompt"]
@@ -203,7 +205,9 @@ def generate(
 
             prompt = [prompt] * image.size(0)
 
-            opt_tokens = self.opt_tokenizer(prompt, return_tensors="pt").to(image.device)
+            opt_tokens = self.opt_tokenizer(prompt, return_tensors="pt").to(
+                image.device
+            )
             input_ids = opt_tokens.input_ids
             attention_mask = torch.cat([atts_opt, opt_tokens.attention_mask], dim=1)
 
@@ -238,7 +242,7 @@ def generate(
 
     @classmethod
     def from_config(cls, cfg):
-        vit_model = cfg.get("vit_model","eva_clip_g")
+        vit_model = cfg.get("vit_model", "eva_clip_g")
         img_size = cfg.get("image_size")
         num_query_token = cfg.get("num_query_token")
         opt_model = cfg.get("opt_model")

lavis/models/blip2_models/blip2_qformer.py

@@ -64,9 +64,9 @@ def __init__(
         )
         if freeze_vit:
             for name, param in self.visual_encoder.named_parameters():
-                param.requires_grad = False                
+                param.requires_grad = False
             self.visual_encoder = self.visual_encoder.eval()
-            self.visual_encoder.train = disabled_train            
+            self.visual_encoder.train = disabled_train
             logging.info("freeze vision encoder")
         self.Qformer, self.query_tokens = self.init_Qformer(
             num_query_token, self.visual_encoder.num_features, cross_attention_freq
@@ -90,7 +90,7 @@ def __init__(
     def forward(self, samples):
         image = samples["image"]
         text = samples["text_input"]
-        
+
         image_embeds = self.ln_vision(self.visual_encoder(image))
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
             image.device
@@ -247,7 +247,7 @@ def forward(self, samples):
             return_dict=True,
             labels=labels,
         )
-        
+
         loss_lm = lm_output.loss
 
         return BlipOutput(
@@ -403,9 +403,9 @@ def extract_features(self, samples, mode="multimodal"):
                 image is not None
             ), "Image is not provided for mode 'image' or 'multimodal'"
             # return query features
-            with torch.cuda.amp.autocast(enabled=(self.device != torch.device("cpu"))):
+            with self.maybe_autocast():
                 image_embeds_frozen = self.ln_vision(self.visual_encoder(image))
-            image_embeds_frozen = image_embeds_frozen.float()      
+            image_embeds_frozen = image_embeds_frozen.float()
             image_atts = torch.ones(
                 image_embeds_frozen.size()[:-1], dtype=torch.long
             ).to(self.device)
@@ -443,9 +443,9 @@ def extract_features(self, samples, mode="multimodal"):
 
         elif mode == "multimodal":
             # return multimodel query features
-            with torch.cuda.amp.autocast(enabled=(self.device != torch.device("cpu"))):
+            with self.maybe_autocast():
                 image_embeds_frozen = self.ln_vision(self.visual_encoder(image))
-            image_embeds_frozen = image_embeds_frozen.float()      
+            image_embeds_frozen = image_embeds_frozen.float()
             image_atts = torch.ones(
                 image_embeds_frozen.size()[:-1], dtype=torch.long
             ).to(self.device)
@@ -482,10 +482,10 @@ def extract_features(self, samples, mode="multimodal"):
 
     @classmethod
     def from_config(cls, cfg):
-        vit_model = cfg.get("vit_model","eva_clip_g")
+        vit_model = cfg.get("vit_model", "eva_clip_g")
         img_size = cfg.get("image_size")
         num_query_token = cfg.get("num_query_token")
-        cross_attention_freq = cfg.get("cross_attention_freq",2)
+        cross_attention_freq = cfg.get("cross_attention_freq", 2)
 
         drop_path_rate = cfg.get("drop_path_rate", 0)
         use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)

lavis/models/blip2_models/blip2_t5.py

@@ -101,7 +101,9 @@ def __init__(
 
     def forward(self, samples):
         image = samples["image"]
-        image_embeds = self.ln_vision(self.visual_encoder(image))
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
             image.device
         )
@@ -117,7 +119,7 @@ def forward(self, samples):
         inputs_t5 = self.t5_proj(query_output.last_hidden_state)
         atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
 
-        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
+        with self.maybe_autocast(dtype=torch.bfloat16):
             input_tokens = self.t5_tokenizer(
                 samples["text_input"],
                 padding="longest",
@@ -182,9 +184,8 @@ def generate(
             captions (list): A list of strings of length batch_size * num_captions.
         """
         image = samples["image"]
-        enable_autocast = self.device != torch.device("cpu")
 
-        with torch.cuda.amp.autocast(enabled=enable_autocast):
+        with self.maybe_autocast():
             image_embeds = self.ln_vision(self.visual_encoder(image))
         image_embeds = image_embeds.float()
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
@@ -220,7 +221,7 @@ def generate(
 
         encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
 
-        with torch.cuda.amp.autocast(enabled=enable_autocast, dtype=torch.bfloat16):
+        with self.maybe_autocast(dtype=torch.bfloat16):
             inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
             inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
 
@@ -257,7 +258,7 @@ def predict_answers(
         **kwargs
     ):
         image = samples["image"]
-        with torch.cuda.amp.autocast(enabled=(self.device != torch.device("cpu"))):
+        with self.maybe_autocast():
             image_embeds = self.ln_vision(self.visual_encoder(image))
         image_embeds = image_embeds.float()
         image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
@@ -288,8 +289,7 @@ def predict_answers(
 
         encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
 
-        device_type = "cuda" if "cuda" in str(self.device) else "cpu"
-        with torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16):
+        with self.maybe_autocast(dtype=torch.bfloat16):
             inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
             inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)