Add Qwen2.5 vLLM generator (based on LlamaGenerator), fix batch 1 iss…

…ue with generator's decode forward (#17422)

models/demos/llama3/tt/generator_vllm.py

@@ -7,7 +7,6 @@
 import torch
 import PIL
 from llama_models.llama3.api.chat_format import create_vision_mask
-from llama_models.llama3.api.tokenizer import Tokenizer
 import ttnn
 
 from models.demos.llama3.tt.generator import LlamaGenerator
@@ -21,6 +20,41 @@
 from vllm.model_executor.models.mllama import MLLAMA_IMAGE_TOKEN_ID, MLLAMA_IMAGE_TOKEN
 
 
+def initialize_vllm_text_transformer(
+    hf_config,
+    mesh_device,
+    max_batch_size,
+    max_seq_len,
+    n_layers=None,
+    dtype=ttnn.bfloat8_b,
+    optimizations=LlamaOptimizations.performance,
+):
+    # Load model args, weights
+    model_args = TtModelArgs(
+        mesh_device,
+        instruct=("Instruct" in hf_config._name_or_path),
+        max_batch_size=max_batch_size,
+        optimizations=optimizations,
+        max_seq_len=max_seq_len,
+    )
+    assert model_args.model_name.replace("-", "") in hf_config._name_or_path.replace(
+        "-", ""
+    ), f"The model specified in vLLM ({hf_config._name_or_path}) does not match the model name ({model_args.model_name}) with model weights ({model_args.DEFAULT_CKPT_DIR})."
+    if n_layers is not None:
+        model_args.n_layers = n_layers
+    state_dict = model_args.load_state_dict()
+
+    tt_model = TtTransformer(
+        args=model_args,
+        mesh_device=mesh_device,
+        dtype=dtype,
+        state_dict=state_dict,
+        weight_cache_path=model_args.weight_cache_path(dtype),
+        use_paged_kv_cache=True,
+    )
+    return tt_model, model_args
+
+
 def input_processor_for_mllama(ctx: InputContext, inputs: Union[DecoderOnlyInputs, EncoderDecoderInputs]):
     """
     Based on vllm.model_executor.models.mllama.py::input_processor_for_mllama().
@@ -140,33 +174,42 @@ def __init__(self, *args, **kwargs):
 
     @classmethod
     def initialize_vllm_model(cls, hf_config, mesh_device, max_batch_size, n_layers=None):
-        instruct_mode = "Instruct" in hf_config._name_or_path
-        max_seq_len = 131072  # TODO: modify this for different models/devices
-        optimizations = LlamaOptimizations.performance  # TODO: maybe change to accuracy
-        dtype = ttnn.bfloat8_b
-
-        # Load model args, weights
-        model_args = TtModelArgs(
+        tt_model, model_args = initialize_vllm_text_transformer(
+            hf_config,
             mesh_device,
-            instruct=instruct_mode,
-            max_batch_size=max_batch_size,
-            optimizations=optimizations,
-            max_seq_len=max_seq_len,
+            max_batch_size,
+            max_seq_len=131072,
+            n_layers=n_layers,
+            dtype=ttnn.bfloat8_b,
+            optimizations=LlamaOptimizations.performance,
         )
-        assert (
-            model_args.model_name in hf_config._name_or_path
-        ), f"The model specified in vLLM ({hf_config._name_or_path}) does not match the model weights ({model_args.DEFAULT_CKPT_DIR})."
-        if n_layers is not None:
-            model_args.n_layers = n_layers
-        state_dict = model_args.load_state_dict()
-
-        tt_model = TtTransformer(
-            args=model_args,
-            mesh_device=mesh_device,
-            dtype=dtype,
-            state_dict=state_dict,
-            weight_cache_path=model_args.weight_cache_path(dtype),
-            use_paged_kv_cache=True,
+        return cls(tt_model, model_args, mesh_device)
+
+    @property
+    def cache_path(self):
+        return self.model_args.model_cache_path
+
+    def prefill_forward(self, *args, **kwargs):
+        return super().prefill_forward_text(*args, **kwargs)
+
+    def decode_forward(self, *args, **kwargs):
+        return super().decode_forward_text(*args, **kwargs)
+
+
+class TtQwen2ForCausalLM(LlamaGenerator):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def initialize_vllm_model(cls, hf_config, mesh_device, max_batch_size, n_layers=None):
+        tt_model, model_args = initialize_vllm_text_transformer(
+            hf_config,
+            mesh_device,
+            max_batch_size,
+            max_seq_len=131072,
+            n_layers=n_layers,
+            dtype=ttnn.bfloat8_b,
+            optimizations=LlamaOptimizations.performance,
         )
         return cls(tt_model, model_args, mesh_device)
 

models/demos/llama3/tt/llama_model.py

@@ -174,8 +174,10 @@ def prepare_decode_inputs_host(self, tokens, current_pos, page_table=None):
         assert current_pos.shape[0] == B, "Batch size mismatch"
         assert B == self.args.max_batch_size, "Batch size must be equal to max_batch_size"
 
+        # Necessary padding to be full tile sized when on device
+        tokens = torch.nn.functional.pad(tokens.view(-1), (0, 32 - len(tokens)), "constant", 0)
         tokens = ttnn.from_torch(
-            tokens.view(-1),
+            tokens,
             device=None,
             dtype=ttnn.uint32,
             mesh_mapper=ttnn.ReplicateTensorToMesh(self.mesh_device),
@@ -254,10 +256,10 @@ def process_output_decode(self, tt_out, B, S=1):
                     num_links=2,
                     cluster_axis=0,
                     mesh_device=self.mesh_device,
-                    topology=ttnn.Topology.Linear,
+                    topology=self.args.ccl_topology(),
                 )
             else:
-                tt_out = ttnn.all_gather(tt_out, dim=3, num_links=1, topology=ttnn.Topology.Linear)
+                tt_out = ttnn.all_gather(tt_out, dim=3, num_links=1, topology=self.args.ccl_topology())
         tt_out = ttnn.untilize(tt_out, use_multicore=True)
         if self.args.num_devices > 1:
             tt_out = ttnn.to_torch(ttnn.get_device_tensors(tt_out)[0]).float()