upgrad minimum torch version to 2.5

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

Author: jiqing-feng

.github/workflows/test_ipex.yml

@@ -19,7 +19,7 @@ jobs:
       fail-fast: false
       matrix:
         transformers-version: ["4.46.0", "4.46.3"]
-        torch-version: ["2.4.0", "2.5.*"]
+        torch-version: ["2.5.*"]
 
     runs-on: ubuntu-22.04
 

optimum/exporters/ipex/modeling_utils.py

@@ -32,8 +32,7 @@
 
 logger = logging.getLogger(__name__)
 
-_IPEX_MINIMUM_VERSION_FOR_PATCHING = "2.4.0"
-_IPEX_MINIMUM_VERSION_FOR_FLASH_VARLEN_ATTN = "2.5.0"
+_IPEX_MINIMUM_VERSION_FOR_PATCHING = "2.5.0"
 
 
 if is_ipex_version("<", _IPEX_MINIMUM_VERSION_FOR_PATCHING):
@@ -213,6 +212,8 @@ def _llama_model_forward(
         position_embeddings = (cos.unsqueeze(1), sin.unsqueeze(1))
     else:
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+    if past_key_values is None:
         attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
             attention_mask=attention_mask,
             input_shape=(input_ids.shape[0], input_ids.shape[-1]),
@@ -334,6 +335,8 @@ def _falcon_model_forward(
         position_embeddings = (cos.unsqueeze(1), sin.unsqueeze(1))
     else:
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+    if past_key_values is None:
         attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
             attention_mask=attention_mask,
             input_shape=(input_ids.shape[0], input_ids.shape[-1]),
@@ -463,6 +466,8 @@ def _gpt2_model_forward(
         hidden_states = (hidden_states.view(-1, hidden_states.shape[-1]))[index]
     else:
         hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+    if past_key_values is None:
         attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
             attention_mask=attention_mask,
             input_shape=(input_ids.shape[0], input_ids.shape[-1]),
@@ -660,11 +665,16 @@ def forward(
 
         if past_len == 0:
             # prefill
-            if past_key_value is None or is_ipex_version("<", _IPEX_MINIMUM_VERSION_FOR_FLASH_VARLEN_ATTN):
+            if past_key_value is None:
+                n_rep = query.shape[1] // key.shape[1]
                 attn_output = torch.nn.functional.scaled_dot_product_attention(
                     query.reshape(input_lens.shape[0], input_lens.max().item(), -1, query.shape[-1]).transpose(1, 2),
-                    key.reshape(input_lens.shape[0], input_lens.max().item(), -1, key.shape[-1]).transpose(1, 2),
-                    value.reshape(input_lens.shape[0], input_lens.max().item(), -1, value.shape[-1]).transpose(1, 2),
+                    key.reshape(input_lens.shape[0], input_lens.max().item(), -1, key.shape[-1])
+                    .transpose(1, 2)
+                    .repeat_interleave(n_rep, 1),
+                    value.reshape(input_lens.shape[0], input_lens.max().item(), -1, value.shape[-1])
+                    .transpose(1, 2)
+                    .repeat_interleave(n_rep, 1),
                     attn_mask=attention_mask,
                     dropout_p=0.0,
                     is_causal=True,

optimum/intel/ipex/modeling_base.py

@@ -299,9 +299,9 @@ def prepare_inputs_for_generation(self, *args, **kwargs):
         return self.model.prepare_inputs_for_generation(*args, **kwargs)
 
     def generate(self, *args, **kwargs):
-        if is_ipex_version("<", "2.4.0") and self._add_patch and kwargs.get("assistant_model", None):
+        if self._add_patch and kwargs.get("assistant_model", None):
             raise ValueError(
-                f"Assisted decoding is not supported for patched models if ipex < 2.4, support methods are {_IPEX_EXPORTED_GENERATION_METHODS}"
+                f"Assisted decoding is not supported for patched models for now, support methods are {_IPEX_EXPORTED_GENERATION_METHODS}"
             )
         # Patch functions to support ipex_paged cache
         if self._add_patch:

setup.py

@@ -66,7 +66,7 @@
     "nncf": ["nncf>=2.14.0"],
     "openvino": ["nncf>=2.14.0", "openvino>=2024.5.0", "openvino-tokenizers>=2024.5.0"],
     "neural-compressor": ["neural-compressor[pt]>3.0", "accelerate", "transformers<4.46"],
-    "ipex": ["intel-extension-for-pytorch>=2.4", "transformers>4.45,<4.47"],
+    "ipex": ["intel-extension-for-pytorch>=2.5", "transformers>4.45,<4.47"],
     "diffusers": ["diffusers"],
     "quality": QUALITY_REQUIRE,
     "tests": TESTS_REQUIRE,

tests/ipex/test_modeling.py

@@ -377,6 +377,9 @@ def test_compare_with_and_without_past_key_values(self):
             outputs_model_without_pkv = model_without_pkv.generate(
                 **tokens, min_new_tokens=self.GENERATION_LENGTH, max_new_tokens=self.GENERATION_LENGTH, num_beams=1
             )
+        import pdb
+
+        pdb.set_trace()
         self.assertTrue(torch.equal(outputs_model_with_pkv, outputs_model_without_pkv))
         self.assertEqual(outputs_model_with_pkv.shape[1], self.GENERATION_LENGTH + tokens.input_ids.shape[1])
         self.assertEqual(outputs_model_without_pkv.shape[1], self.GENERATION_LENGTH + tokens.input_ids.shape[1])