fix incorrect output in qwen2 idefics if hpu graph is used

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

backends/gaudi/server/text_generation_server/layers/rotary.py

@@ -95,7 +95,10 @@ class PositionRotaryEmbedding(nn.Module):
                 mrope_section = rope_scaling["mrope_section"]
                 if mrope_section is not None:
                     return RotaryPositionEmbeddingMultimodalSections(
-                        inv_freq, scaling_factor, mrope_section
+                        inv_freq,
+                        scaling_factor,
+                        mrope_section,
+                        config.max_position_embeddings,
                     )
             elif rope_type == "dynamic":
                 scaling_factor = rope_scaling["factor"]
@@ -557,8 +560,13 @@ def apply_llama3_scaling(
 
 
 class RotaryPositionEmbeddingMultimodalSections(PositionRotaryEmbedding):
-    def __init__(self, inv_freq: torch.Tensor, scaling_factor: float, sections: list):
-        super().__init__(inv_freq, scaling_factor)
+    def __init__(
+        self,
+        inv_freq: torch.Tensor,
+        scaling_factor: float,
+        sections: list,
+        max_position_embeddings,
+    ):
         self.sections = sections
         self._cos_cached = None
         self._sin_cached = None
@@ -568,6 +576,7 @@ class RotaryPositionEmbeddingMultimodalSections(PositionRotaryEmbedding):
             .view(1, 1, -1)
             .to(inv_freq.device)
         )
+        super().__init__(inv_freq, scaling_factor, max_position_embeddings)
 
     def _update_cos_sin_cache(
         self, dtype: torch.dtype, device: torch.device, seqlen: int

backends/gaudi/server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py

@@ -110,6 +110,7 @@ class PaliGemmaForConditionalGeneration(nn.Module):
             slots=slots,
             seqlen=seqlen,
             hpu_attention_meta=hpu_attention_meta,
+            prefill_cache_indices=None,
         )
 
         if lm_head_indices is not None:

backends/gaudi/server/text_generation_server/models/custom_modeling/idefics2.py

@@ -728,7 +728,8 @@ class Idefics2ForConditionalGeneration(nn.Module):
     ):
         """In place merges in vision_embeddings with inputs_embeds."""
         # mask = input_ids == self.config.image_token_index
-        mask = input_ids == self.config.image_token_id
+        #  - replace `==` with torch.where to fix the issue in hpu graph
+        mask = torch.where(input_ids == self.config.image_token_id)
         # Let's pray we have enabled enough slots !
         inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
         return inputs_embeds
@@ -794,6 +795,7 @@ class Idefics2ForConditionalGeneration(nn.Module):
                     ].contiguous()
 
                 patch_size = self.config.vision_config.patch_size
+                """
                 patches_subgrid = pixel_attention_mask.unfold(
                     dimension=1, size=patch_size, step=patch_size
                 )
@@ -801,6 +803,21 @@ class Idefics2ForConditionalGeneration(nn.Module):
                     dimension=2, size=patch_size, step=patch_size
                 )
                 patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+                """
+                # hpu does none support unfold
+                conv_kernel = torch.ones(
+                    [1, 1, patch_size, patch_size],
+                    dtype=pixel_values.dtype,
+                    device=pixel_values.device,
+                )
+                patches_subgrid = torch.nn.functional.conv2d(
+                    pixel_attention_mask.unsqueeze(1).to(conv_kernel.dtype),
+                    conv_kernel,
+                    stride=patch_size,
+                ).squeeze(1)
+                patch_attention_mask = torch.eq(
+                    patches_subgrid, (patch_size * patch_size)
+                )
 
                 # Get sequence from the vision encoder
                 image_hidden_states = self.vision_model(

backends/gaudi/server/text_generation_server/models/custom_modeling/idefics3.py

@@ -471,7 +471,8 @@ class Idefics3ForConditionalGeneration(nn.Module):
     ):
         """In place merges in vision_embeddings with inputs_embeds."""
         # mask = input_ids == self.config.image_token_index
-        mask = input_ids == self.config.image_token_id
+        #  - replace `==` with torch.where to fix the issue in hpu graph
+        mask = torch.where(input_ids == self.config.image_token_id)
         # Let's pray we have enabled enough slots !
         inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1])
         return inputs_embeds
@@ -539,6 +540,7 @@ class Idefics3ForConditionalGeneration(nn.Module):
                     ].contiguous()
 
                 patch_size = self.config.vision_config.patch_size
+                """
                 patches_subgrid = pixel_attention_mask.unfold(
                     dimension=1, size=patch_size, step=patch_size
                 )
@@ -546,6 +548,21 @@ class Idefics3ForConditionalGeneration(nn.Module):
                     dimension=2, size=patch_size, step=patch_size
                 )
                 patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
+                """
+                # hpu does none support unfold
+                conv_kernel = torch.ones(
+                    [1, 1, patch_size, patch_size],
+                    dtype=pixel_values.dtype,
+                    device=pixel_values.device,
+                )
+                patches_subgrid = torch.nn.functional.conv2d(
+                    pixel_attention_mask.unsqueeze(1).to(conv_kernel.dtype),
+                    conv_kernel,
+                    stride=patch_size,
+                ).squeeze(1)
+                patch_attention_mask = torch.eq(
+                    patches_subgrid, (patch_size * patch_size)
+                )
 
                 # Get sequence from the vision encoder
                 image_hidden_states = self.vision_model(

backends/gaudi/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py

@@ -739,10 +739,12 @@ class Qwen2_5VisionModel(nn.Module):
 
         cu_window_seqlens = torch.tensor(
             cu_window_seqlens,
-            device=hidden_states.device,
+            device="cpu",
             dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
         )
-        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens).to(
+            hidden_states.device
+        )
 
         # create a cu_seqlens tensor to be used in the attention mask
         cu_seqlens = torch.repeat_interleave(
@@ -928,7 +930,8 @@ class Qwen2_5VLForConditionalGeneration(nn.Module):
                 image_embeds = self.visual(
                     pixel_values, grid_thw=image_grid_thw
                 ).squeeze(0)
-                inputs_embeds[input_ids == self.image_token_id] = image_embeds
+                mask = torch.where(input_ids == self.image_token_id)
+                inputs_embeds[mask] = image_embeds
 
         hidden_states = self.text_model(
             inputs_embeds=inputs_embeds,

backends/gaudi/server/text_generation_server/models/custom_modeling/qwen2_vl.py

@@ -503,7 +503,8 @@ class Qwen2VLForConditionalGeneration(nn.Module):
                 image_embeds = self.visual(
                     pixel_values, grid_thw=image_grid_thw
                 ).squeeze(0)
-                inputs_embeds[input_ids == self.image_token_id] = image_embeds
+                mask = torch.where(input_ids == self.image_token_id)
+                inputs_embeds[mask] = image_embeds
 
         hidden_states = self.text_model(
             inputs_embeds=inputs_embeds,

backends/gaudi/server/text_generation_server/models/custom_modeling/vlm.py

@@ -16,7 +16,7 @@ def load_text_model(prefix, config, weights, name=None):
             FlashGemmaForCausalLM,
         )
 
-        return FlashGemmaForCausalLM(prefix, config, weights, causal=False)
+        return FlashGemmaForCausalLM(prefix, config, weights)
     elif config.model_type == "gemma2":
         from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
             FlashGemma2ForCausalLM,