Merge branch 'turboderp-org:master' into master

Author: Originalimoc

.github/workflows/build-wheels-release-linux.yml

@@ -51,7 +51,7 @@ class Model:
     current_image: Image or None = None
     current_description: str
 
-    def __init__(self, model_directory):
+    def __init__(self, model_directory, bbox_mode: str):
         self.model_directory = model_directory
         self.config = None
         self.vision_model = None
@@ -61,17 +61,22 @@ def __init__(self, model_directory):
         self.current_image = None
         self.current_emb = None
         self.current_description = ""
+        bbox_funcs = {
+            "qwen2": self.get_grounding_bb_qwen2,
+            "qwen25": self.get_grounding_bb_qwen25,
+        }
+        self.bbox_func = bbox_funcs[bbox_mode]
 
     def load(self):
         """Load and initialize the things"""
         self.config = ExLlamaV2Config(self.model_directory)
-        self.config.max_seq_len = 16384
+        self.config.max_seq_len = 8192
 
         self.vision_model = ExLlamaV2VisionTower(self.config)
         self.vision_model.load(progress = True)
 
         self.model = ExLlamaV2(self.config)
-        self.cache = ExLlamaV2Cache(self.model, lazy = True, max_seq_len = 16384)
+        self.cache = ExLlamaV2Cache(self.model, lazy = True, max_seq_len = 32768)
         self.model.load_autosplit(self.cache, progress = True)
         self.tokenizer = ExLlamaV2Tokenizer(self.config)
 
@@ -148,14 +153,21 @@ def inference(self, settext_fn, update_fn):
                 lastupdate = time.time()
                 settext_fn(text)
                 update_fn()
+#
+#         text = \
+# """And you may find yourself living in a shotgun shack
+# And you may find yourself in another part of the world
+# And you may find yourself behind the wheel of a large automobile
+# And you may find yourself in a beautiful house, with a beautiful wife
+# And you may ask yourself, "Well, how did I get here?\""""
 
         settext_fn(text)
         update_fn()
         self.current_description = text
         print("Image description from model:")
         print(text)
 
-    def get_grounding_bb(self, start, end) -> tuple:
+    def get_grounding_bb_qwen2(self, start, end) -> tuple:
         """
         Prompt the model again and try to extraxt the bounding box of the image details indicated by selected portion
         of the description. We do this by repeating the exact same prompt up to and including the selected text, but
@@ -209,6 +221,55 @@ def get_grounding_bb(self, start, end) -> tuple:
 
         return a, b
 
+    def get_grounding_bb_qwen25(self, start, end) -> tuple:
+        """
+        Qwen2.5 works the same way, except the coordinates are no longer normalized and the format is:
+        "(x0,y0,x1,y1)"
+        """
+
+        if start >= end:
+            return None, None
+
+        # Including leading space
+        if start > 0 and self.current_description[start - 1] == " ":
+            start -= 1
+
+        # Repeat the same prompt up to the selection, with grounding tokens added
+        prompt = self.get_prompt()
+        prompt += self.current_description[:start]
+        prompt += "<|object_ref_start|>"
+        prompt += self.current_description[start:end]
+        prompt += "<|object_ref_end|><|box_start|>("
+
+        bb_string, res = self.generator.generate(
+            prompt = prompt,
+            add_bos = True,
+            max_new_tokens = 28,
+            stop_conditions = [self.tokenizer.single_id("<|box_end|>")],
+            gen_settings = ExLlamaV2Sampler.Settings.greedy(),
+            embeddings = [self.current_emb],
+            completion_only = True,
+            return_last_results = True,  # debug purposes
+        )
+        bb_string = "(" + bb_string
+
+        print(f"Generation: {bb_string}")
+        pprint.pprint(res, indent = 4)
+
+        # BB string is in the format "(x0,y0,x1,y1)" with integer coordinates
+
+        s = self.current_image.size
+        try:
+            d = tuple(map(int, bb_string.strip("()").split(",")))
+            a = (d[0] / s[0], d[1] / s[1])
+            b = (d[2] / s[0], d[3] / s[1])
+        except:
+            print("No bounding box could be determined")
+            a, b = None, None
+
+        return a, b
+
+
 
 class GroundingDemo(QMainWindow):
 
@@ -472,7 +533,7 @@ def on_selection_made(self, pos):
 
         print(f"Selected span: {start}, {end}")
         print(f"Selected text: {repr(self.model.current_description[start:end])}")
-        a, b = self.model.get_grounding_bb(start, end)
+        a, b = self.model.bbox_func(start, end)
         self.image_label.set_bounding_box(a, b)
 
 
@@ -481,9 +542,14 @@ def on_selection_made(self, pos):
 #   https://huggingface.co/turboderp/Qwen2-VL-7B-Instruct-exl2
 
 def main():
-    model_dir = "/mnt/str/models/qwen2-vl-7b-instruct-exl2/6.0bpw"
+
+    # model_dir = "/mnt/str/models/qwen2-vl-7b-instruct-exl2/6.0bpw"
+    # bbox_mode = "qwen25"
+    model_dir = "/mnt/str/models/qwen2.5-vl-7b-instruct-exl2/6.0bpw"
+    bbox_mode = "qwen25"
+
     app = QApplication(sys.argv)
-    model = Model(model_dir)
+    model = Model(model_dir, bbox_mode)
     model.load()
     window = GroundingDemo(model, model_dir)
     window.show()

.github/workflows/build-wheels-release-rocm62.yml

@@ -356,7 +356,7 @@ class Params:
 
         # Qwen2-VL (2, 2.5)
 
-        if arch_string == "Qwen2VLForConditionalGeneration":
+        if arch_string in ["Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration"]:
             arch_recognized = True
             self.lm.layer_keys += \
                 layer_keys_llama_norms + \
@@ -368,27 +368,44 @@ class Params:
             self.lm.mrope = True
             self.lm.rope_freq_half = True
 
-            read_config["vision_config"].update({"model_type": "qwen2"})
             self.vt_prefix = "visual."
-            self.vt.keys.update({
-                "fused_qkv": ".attn.qkv",
-                "attn_o": ".attn.proj",
-                "mlp_gate": None,
-                "mlp_up": ".mlp.fc1",
-                "mlp_down": ".mlp.fc2",
-                "norm_1": ".norm1",
-                "norm_2": ".norm2",
-                "layers": "blocks",
-                "patch_conv": "patch_embed.proj",
-            })
-            self.vt.mlp_gate = False
+            if arch_string == "Qwen2VLForConditionalGeneration":
+                read_config["vision_config"].update({"model_type": "qwen2"})
+                self.vt.keys.update({
+                    "fused_qkv": ".attn.qkv",
+                    "attn_o": ".attn.proj",
+                    "mlp_gate": None,
+                    "mlp_up": ".mlp.fc1",
+                    "mlp_down": ".mlp.fc2",
+                    "norm_1": ".norm1",
+                    "norm_2": ".norm2",
+                    "layers": "blocks",
+                    "patch_conv": "patch_embed.proj",
+                })
+                self.vt.mlp_gate = False
+                self.vt.mlp_act_func = "quickgelu"
+                self.vt.norm = "layernorm"
+            elif arch_string == "Qwen2_5_VLForConditionalGeneration":
+                read_config["vision_config"].update({"model_type": "qwen2.5"})
+                self.vt.keys.update({
+                    "fused_qkv": ".attn.qkv",
+                    "attn_o": ".attn.proj",
+                    "mlp_gate": ".mlp.gate_proj",
+                    "mlp_up": ".mlp.up_proj",
+                    "mlp_down": ".mlp.down_proj",
+                    "norm_1": ".norm1",
+                    "norm_2": ".norm2",
+                    "layers": "blocks",
+                    "patch_conv": "patch_embed.proj",
+                })
+                self.vt.mlp_gate = True
+                self.vt.mlp_act_func = "silu"
+                self.vt.norm = "rmsnorm"
             self.vt.mlp_bias = True
             self.vt.attention_bias_qkv = True
             self.vt.attention_bias_o = True
             self.vt.vision_input_norm = False
             self.vt.vision_conv3d = True
-            self.vt.mlp_act_func = "quickgelu"
-            self.vt.norm = "layernorm"
 
             self.mmp_prefix = "visual.merger."
             self.mmp.keys.update({

.github/workflows/build-wheels-release.yml

@@ -41,11 +41,11 @@
             print(" ## Warning: Flash Attention is installed but unsupported GPUs were detected.")
 
         if [2, 2, 1] <= flash_attn_ver < [2, 5, 7]:
-            from flash_attn import flash_attn_func
+            from flash_attn import flash_attn_func, flash_attn_varlen_func
             has_flash_attn = True
 
         if [2, 5, 7] <= flash_attn_ver:
-            from flash_attn import flash_attn_func, flash_attn_with_kvcache
+            from flash_attn import flash_attn_func, flash_attn_varlen_func, flash_attn_with_kvcache
             # import flash_attn_2_cuda as flash_attn_cuda
 
             signature = list(inspect.signature(flash_attn_func).parameters)
@@ -882,7 +882,9 @@ def _attn_torch(self, batch_size, q_len, q_states, k_states, v_states, attn_para
                 k_states = k_states[:, :, -self.sliding_window:, :]
                 v_states = v_states[:, :, -self.sliding_window:, :]
 
-            if attn_params.is_causal():
+            if self.layer_idx in attn_params.block_diag_layers:
+                attn_mask_lr = attn_params.get_block_diag_mask(q_states.device)
+            elif attn_params.is_causal():
                 attn_mask_lr = causal_lower_right(q_len, k_states.shape[2])
             else:
                 attn_mask_lr = attn_params.get_attn_mask(q_states.device)
@@ -904,7 +906,9 @@ def _attn_torch(self, batch_size, q_len, q_states, k_states, v_states, attn_para
             attn_weights = torch.matmul(q_states, k_states)
 
             attn_weights *= self.scaling
-            if causal:
+            if self.layer_idx in attn_params.block_diag_layers:
+                attn_mask = attn_params.get_block_diag_mask(attn_weights.device)
+            elif causal:
                 attn_mask = attn_params.get_attn_mask(attn_weights.device)
 
             if cfg.attn_logit_softcapping:
@@ -939,14 +943,30 @@ def _attn_flash(self, batch_size, q_len, q_states, k_states, v_states, attn_para
             if has_flash_attn_with_softcap:
                 flash_kwargs["softcap"] = cfg.attn_logit_softcapping
 
-        attn_output = flash_attn_func(
-            q_states,
-            k_states,
-            v_states,
-            causal = causal,
-            softmax_scale = self.scaling,
-            **flash_kwargs
-        )
+        if self.layer_idx in attn_params.block_diag_layers:
+            q_states = q_states.flatten(start_dim = 0, end_dim = 1)
+            k_states = k_states.flatten(start_dim = 0, end_dim = 1)
+            v_states = v_states.flatten(start_dim = 0, end_dim = 1)
+            max_seqlen = attn_params.get_cu_seqlens_max()
+            cu_seqlens = attn_params.get_cu_seqlens(self.device_idx)
+            attn_output = flash_attn_varlen_func(
+                q_states,
+                k_states,
+                v_states,
+                cu_seqlens,
+                cu_seqlens,
+                max_seqlen,
+                max_seqlen
+            )
+        else:
+            attn_output = flash_attn_func(
+                q_states,
+                k_states,
+                v_states,
+                causal = causal,
+                softmax_scale = self.scaling,
+                **flash_kwargs
+            )
         attn_output = attn_output.reshape((batch_size, q_len, self.num_attention_heads * self.head_dim))
         return attn_output
 

examples/multimodal_grounding_qwen.py

@@ -21,6 +21,10 @@ class Params:
     alt_rope_embed_dict: dict | None
     rope_offsets: torch.Tensor | None
     non_causal_attn: bool
+    block_diag_layers: set
+    block_diag_mask: torch.Tensor | None
+    cu_seqlens: torch.Tensor | None
+    cu_seqlens_max: int | None
 
     def __init__(
         self,
@@ -66,6 +70,11 @@ def __init__(
         self.past_len_tp = None
         self.paged = paged
 
+        self.block_diag_layers = set()
+        self.block_diag_mask = None
+        self.cu_seqlens = None
+        self.cu_seqlens_max = None
+
     def is_causal(self) -> bool:
         return self.input_mask is None
 
@@ -164,6 +173,31 @@ def get_rope_offsets(self, device_idx: int) -> torch.Tensor | None:
             self.rope_offsets = safe_move_tensor(self.rope_offsets, device_idx, non_blocking = True)
         return self.rope_offsets
 
+    def get_cu_seqlens(self, device: int) -> torch.Tensor | None:
+        if self.cu_seqlens is None:
+            return None
+        if self.cu_seqlens.device.index != device:
+            self.cu_seqlens = safe_move_tensor(self.cu_seqlens, device, non_blocking = True)
+        return self.cu_seqlens
+
+    def get_cu_seqlens_max(self) -> torch.Tensor | None:
+        assert self.cu_seqlens is not None
+        if self.cu_seqlens_max is not None:
+            return self.cu_seqlens_max
+        self.cu_seqlens_max = (self.cu_seqlens[1:] - self.cu_seqlens[:-1]).max().item()
+        return self.cu_seqlens_max
+
+    def get_block_diag_mask(self, device: int) -> torch.Tensor | None:
+        if self.block_diag_mask is None:
+            csl = self.get_cu_seqlens(device)
+            if csl is None:
+                return None
+            positions = torch.arange(csl[-1], device = csl.device)
+            labels = torch.searchsorted(csl[1:], positions, right = True)
+            self.block_diag_mask = labels.unsqueeze(0) == labels.unsqueeze(1).repeat(self.batch_size)
+        if self.block_diag_mask.device.index != device:
+            self.block_diag_mask = safe_move_tensor(self.block_diag_mask, device, non_blocking = True)
+        return self.block_diag_mask
 
 
 class PagedParams(Params):