[Misc]Add customized information for models

tests/models/test_big_models.py

@@ -43,3 +43,18 @@ def test_models(
             f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
         assert hf_output_ids == vllm_output_ids, (
             f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+def test_model_print(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    vllm_model = vllm_runner(model, dtype=dtype)
+    # This test is for verifying whether the model's extra_repr
+    # can be printed correctly.
+    print(vllm_model.model.llm_engine.model_executor.driver_worker.
+          model_runner.model)
+    del vllm_model

tests/models/test_models.py

@@ -49,3 +49,18 @@ def test_models(
             f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
         assert hf_output_ids == vllm_output_ids, (
             f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float"])
+def test_model_print(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    vllm_model = vllm_runner(model, dtype=dtype)
+    # This test is for verifying whether the model's extra_repr
+    # can be printed correctly.
+    print(vllm_model.model.llm_engine.model_executor.driver_worker.
+          model_runner.model)
+    del vllm_model

vllm/attention/layer.py

@@ -47,3 +47,10 @@ def forward(
     ) -> torch.Tensor:
         return self.impl.forward(query, key, value, kv_cache, attn_metadata,
                                  kv_scale)
+
+    def extra_repr(self) -> str:
+        s = f"head_size={self.impl.head_size}"  # type: ignore
+        s += f", num_heads={self.impl.num_heads}"  # type: ignore
+        s += f", num_kv_heads={self.impl.num_kv_heads}"  # type: ignore
+        s += f", scale={self.impl.scale}"  # type: ignore
+        return s

vllm/model_executor/layers/activation.py

@@ -67,6 +67,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             ops.gelu_tanh_and_mul(out, x)
         return out
 
+    def extra_repr(self) -> str:
+        return f'approximate={repr(self.approximate)}'
+
 
 class NewGELU(nn.Module):
 

vllm/model_executor/layers/layernorm.py

@@ -64,3 +64,8 @@ def forward(
             self.variance_epsilon,
         )
         return out
+
+    def extra_repr(self) -> str:
+        s = f"hidden_size={self.weight.data.size(0)}"
+        s += f", eps={self.variance_epsilon}"
+        return s

vllm/model_executor/layers/linear.py

@@ -181,6 +181,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         output_bias = self.bias if self.skip_bias_add else None
         return output, output_bias
 
+    def extra_repr(self) -> str:
+        s = f"in_features={self.input_size}"
+        s += f", output_features={self.output_size}"
+        s += f", bias={self.bias is not None}"
+        return s
+
 
 class ColumnParallelLinear(LinearBase):
     """Linear layer with column parallelism.
@@ -281,6 +287,14 @@ def forward(self, input_):
         output_bias = self.bias if self.skip_bias_add else None
         return output, output_bias
 
+    def extra_repr(self) -> str:
+        s = f"in_features={self.input_size}"
+        s += f", output_features={self.output_size_per_partition}"
+        s += f", bias={self.bias is not None}"
+        s += f", tp_size={get_tensor_model_parallel_world_size()}"
+        s += f", gather_output={self.gather_output}"
+        return s
+
 
 class MergedColumnParallelLinear(ColumnParallelLinear):
     """Packed linear layers with column parallelism.
@@ -685,3 +699,11 @@ def forward(self, input_):
             output = output_
             output_bias = self.bias
         return output, output_bias
+
+    def extra_repr(self) -> str:
+        s = f"input_features={self.input_size_per_partition}"
+        s += f", output_features={self.output_size}"
+        s += f", bias={self.bias is not None}"
+        s += f", tp_size={self.tp_size}"
+        s += f", reduce_results={self.reduce_results}"
+        return s

vllm/model_executor/layers/logits_processor.py

@@ -70,6 +70,12 @@ def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
             logits = logits[:, :self.org_vocab_size]
         return logits
 
+    def extra_repr(self) -> str:
+        s = f"vocab_size={self.vocab_size}"
+        s += f", forg_vocab_size={self.org_vocab_size}"
+        s += f", scale={self.scale}, logits_as_input={self.logits_as_input}"
+        return s
+
 
 def _prune_hidden_states(
     hidden_states: torch.Tensor,

vllm/model_executor/layers/rotary_embedding.py

@@ -156,6 +156,12 @@ def forward(
                                  self.cos_sin_cache, self.is_neox_style)
         return query, key
 
+    def extra_repr(self) -> str:
+        s = f"head_size={self.head_size}, rotary_dim={self.rotary_dim}"
+        s += f", max_position_embeddings={self.max_position_embeddings}"
+        s += f", base={self.base}, is_neox_style={self.is_neox_style}"
+        return s
+
 
 class LinearScalingRotaryEmbedding(RotaryEmbedding):
     """RotaryEmbedding extended with linear scaling.

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -105,6 +105,14 @@ def forward(self, input_):
         output = tensor_model_parallel_all_reduce(output_parallel)
         return output
 
+    def extra_repr(self) -> str:
+        s = f"num_embeddings={self.num_embeddings_per_partition}"
+        s += f", embedding_dim={self.embedding_dim}"
+        s += f", org_vocab_size={self.org_vocab_size}"
+        s += f', num_embeddings_padded={self.num_embeddings_padded}'
+        s += f', tp_size={self.tp_size}'
+        return s
+
 
 class ParallelLMHead(VocabParallelEmbedding):
     """Parallelized LM head.