Support for Falcon2-11B (#30771)

* remove unrelated changes

* remove unrelated changes on phi and stable LM

* add: Test for Falcon 10B

* fix: formatting

* fix: loading the falcon 10B in 8 bit precision using bitsanbytes.

* fix: device placement

* fix: broken tests.

* fix: backwards compatibility for falcon 1B architecture.

* chore: updated test.

* chore: test_modeling_falcon.py to use the 11B model.

* chore: minor edit

* chore: formating.

---------
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: ArthurZucker <arthur.zucker@gmail.com>

src/transformers/models/falcon/configuration_falcon.py

@@ -42,6 +42,9 @@ class FalconConfig(PretrainedConfig):
             Number of hidden layers in the Transformer decoder.
         num_attention_heads (`int`, *optional*, defaults to 71):
             Number of attention heads for each attention layer in the Transformer encoder.
+        num_ln_in_parallel_attn (`int`, *optional*):
+            Set to 2 if separate layer norms are to be used for the MLP and the attention output when using parallel
+            attention, otherwise, 1.
         layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
             The epsilon used by the layer normalization layers.
         initializer_range (`float`, *optional*, defaults to 0.02):
@@ -115,6 +118,7 @@ class FalconConfig(PretrainedConfig):
         hidden_size=4544,
         num_hidden_layers=32,
         num_attention_heads=71,
+        num_ln_in_parallel_attn=None,
         layer_norm_epsilon=1e-5,
         initializer_range=0.02,
         use_cache=True,
@@ -154,6 +158,7 @@ class FalconConfig(PretrainedConfig):
         self.multi_query = multi_query  # Ignored when new_decoder_architecture is True
         self.parallel_attn = parallel_attn
         self.bias = bias
+        self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
         self.max_position_embeddings = max_position_embeddings
         self.rope_theta = rope_theta
         self.rope_scaling = rope_scaling

src/transformers/models/falcon/modeling_falcon.py

@@ -767,15 +767,20 @@ class FalconDecoderLayer(nn.Module):
         self.hidden_dropout = config.hidden_dropout
         self.config = config
 
-        if config.new_decoder_architecture:
-            # The layer norm before self-attention
-            self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-            # The layer norm before the MLP
-            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        else:
+        if config.num_ln_in_parallel_attn is None and config.new_decoder_architecture:
+            config.num_ln_in_parallel_attn = 2
+
+        if not config.parallel_attn:
+            self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
             self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-            if not config.parallel_attn:
-                self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        else:
+            if config.num_ln_in_parallel_attn == 2:
+                # The layer norm before self-attention
+                self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+                # The layer norm before the MLP
+                self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+            else:
+                self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
 
     def forward(
         self,
@@ -796,7 +801,7 @@ class FalconDecoderLayer(nn.Module):
 
         residual = hidden_states
 
-        if self.config.new_decoder_architecture:
+        if self.config.new_decoder_architecture and self.config.num_ln_in_parallel_attn == 2:
             attention_layernorm_out = self.ln_attn(hidden_states)
             mlp_layernorm_out = self.ln_mlp(hidden_states)
         else:
@@ -826,6 +831,13 @@ class FalconDecoderLayer(nn.Module):
                 )
                 mlp_layernorm_out = self.post_attention_layernorm(residual)
 
+        if (
+            self.config.new_decoder_architecture
+            and self.config.parallel_attn
+            and self.config.num_ln_in_parallel_attn == 1
+        ):
+            mlp_layernorm_out = attention_layernorm_out
+
         outputs = attn_outputs[1:]
 
         # MLP.

tests/models/falcon/test_modeling_falcon.py

@@ -602,6 +602,25 @@ class FalconLanguageGenerationTest(unittest.TestCase):
 
         self.assertEqual(output_str, EXPECTED_OUTPUT)
 
+    @slow
+    @require_bitsandbytes
+    def test_lm_generate_falcon_11b(self):
+        tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-11B", padding_side="left")
+        model = FalconForCausalLM.from_pretrained(
+            "tiiuae/falcon-11B", device_map={"": torch_device}, load_in_8bit=True
+        )
+        model.eval()
+        inputs = tokenizer(
+            "Two roads diverged in a yellow wood,", return_tensors="pt", return_token_type_ids=False
+        ).to(torch_device)
+
+        EXPECTED_OUTPUT = "Two roads diverged in a yellow wood,\nAnd sorry I could not travel both\n"
+
+        output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=9)
+        output_str = tokenizer.batch_decode(output_ids)[0]
+
+        self.assertEqual(output_str, EXPECTED_OUTPUT)
+
     @slow
     def test_lm_generation_big_models(self):
         # The big models are way too big for the CI, so we use tiny random models that resemble their
@@ -647,7 +666,7 @@ class FalconLanguageGenerationTest(unittest.TestCase):
         tokenizer.pad_token = tokenizer.eos_token
         model = AutoModelForCausalLM.from_pretrained(
             "tiiuae/falcon-7b",
-            device_map="auto",
+            device_map={"": torch_device},
             load_in_4bit=True,
         )