[Feature] Add async tensor parallelism for scaled mm (vllm-project#20155)

Signed-off-by: cascade812 <[email protected]>
Signed-off-by: x22x22 <[email protected]>

Author: cascade812

tests/compile/test_async_tp.py

@@ -22,6 +22,8 @@
                      multi_gpu_test)
 from .backend import TestBackend
 
+FP8_DTYPE = current_platform.fp8_dtype()
+
 prompts = [
     "Hello, my name is",
     "The president of the United States is",
@@ -32,9 +34,10 @@
 
 class TestMMRSModel(torch.nn.Module):
 
-    def __init__(self, hidden_size=16):
+    def __init__(self, hidden_size=16, dtype=torch.float16):
         super().__init__()
         self.hidden_size = hidden_size
+        self.dtype = dtype
         self.gate_proj = torch.nn.Parameter(torch.empty(
             (self.hidden_size * 2, hidden_size)),
                                             requires_grad=False)
@@ -64,9 +67,10 @@ def ops_in_model_after(self):
 
 class TestAGMMModel(torch.nn.Module):
 
-    def __init__(self, hidden_size=16):
+    def __init__(self, hidden_size=16, dtype=torch.float16):
         super().__init__()
         self.hidden_size = hidden_size
+        self.dtype = dtype
         self.weight = torch.nn.Parameter(torch.empty(
             (hidden_size, hidden_size)),
                                          requires_grad=False)
@@ -91,8 +95,125 @@ def ops_in_model_after(self):
         return [torch.ops.symm_mem.fused_all_gather_matmul.default]
 
 
+class _BaseScaledMMModel(torch.nn.Module):
+
+    def __init__(self, hidden_size=16, dtype=torch.float16):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.dtype = dtype
+        self.weight = torch.empty([hidden_size, hidden_size], dtype=FP8_DTYPE)\
+            .contiguous().transpose(0, 1)
+
+        # Initialize scale_b for _scaled_mm.
+        self.scale_b = torch.ones(1, self.hidden_size, dtype=torch.float32)
+
+
+class TestScaledMMRSModel(_BaseScaledMMModel):
+
+    def forward(self, input: torch.Tensor):
+        """
+        Forward pass implementing the scaled_mm + reduce scatter in the FX graph
+    
+        """
+        fp8_input = input.to(FP8_DTYPE)
+        scale_a = torch.ones(input.shape[0], 1, dtype=torch.float32)
+        scaled_mm = torch._scaled_mm(fp8_input,
+                                     self.weight,
+                                     scale_a=scale_a,
+                                     scale_b=self.scale_b,
+                                     out_dtype=self.dtype)
+        reduce_scatter = tensor_model_parallel_reduce_scatter(scaled_mm, dim=0)
+        return reduce_scatter
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.reduce_scatter.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter.default]
+
+
+class TestAGScaledMMModel(_BaseScaledMMModel):
+
+    def forward(self, input: torch.Tensor):
+        """
+        Forward pass implementing the all gather + scaled_mm in the FX graph
+        """
+        # Reshape input
+        fp8_input = input.to(FP8_DTYPE)
+        all_gather = tensor_model_parallel_all_gather(fp8_input, dim=0)
+
+        scale_a = torch.ones(all_gather.shape[0], 1, dtype=torch.float32)
+        scaled_mm = torch._scaled_mm(all_gather,
+                                     self.weight,
+                                     scale_a=scale_a,
+                                     scale_b=self.scale_b,
+                                     out_dtype=self.dtype)
+        return scaled_mm
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.all_gather.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_all_gather_scaled_matmul.default]
+
+
+class TestCutlassScaledMMRSModel(_BaseScaledMMModel):
+
+    def forward(self, input: torch.Tensor):
+        """
+        Forward pass implementing the cutlass_scaled_mm + reduce scatter
+        in the FX graph
+    
+        """
+        fp8_input = input.to(FP8_DTYPE)
+        scale_a = torch.ones(input.shape[0], 1, dtype=torch.float32)
+        mm_out = torch.empty((fp8_input.shape[0], self.weight.shape[1]),
+                             dtype=self.dtype,
+                             device=input.device)
+        torch.ops._C.cutlass_scaled_mm(mm_out, fp8_input, self.weight, scale_a,
+                                       self.scale_b, None)
+        reduce_scatter = tensor_model_parallel_reduce_scatter(mm_out, dim=0)
+        return reduce_scatter
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.reduce_scatter.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_scaled_matmul_reduce_scatter.default]
+
+
+class TestAGCutlassScaledMMModel(_BaseScaledMMModel):
+
+    def forward(self, input: torch.Tensor):
+        """
+        Forward pass implementing the all gather + cutlass_scaled_mm 
+        in the FX graph
+        """
+        # Reshape input
+        fp8_input = input.to(FP8_DTYPE)
+        all_gather = tensor_model_parallel_all_gather(fp8_input, dim=0)
+
+        scale_a = torch.ones(all_gather.shape[0], 1, dtype=torch.float32)
+
+        mm_out = torch.empty((all_gather.shape[0], self.weight.shape[1]),
+                             dtype=self.dtype,
+                             device=all_gather.device)
+        torch.ops._C.cutlass_scaled_mm(mm_out, all_gather, self.weight,
+                                       scale_a, self.scale_b, None)
+        return mm_out
+
+    def ops_in_model_before(self):
+        return [torch.ops.vllm.all_gather.default]
+
+    def ops_in_model_after(self):
+        return [torch.ops.symm_mem.fused_all_gather_scaled_matmul.default]
+
+
 @multi_gpu_test(num_gpus=2)
-@pytest.mark.parametrize("test_model", [TestMMRSModel, TestAGMMModel])
+@pytest.mark.parametrize("test_model", [
+    TestMMRSModel, TestAGMMModel, TestScaledMMRSModel, TestAGScaledMMModel,
+    TestCutlassScaledMMRSModel, TestAGCutlassScaledMMModel
+])
 @pytest.mark.parametrize("batch_size", [8])
 @pytest.mark.parametrize("seq_len", [16])
 @pytest.mark.parametrize("hidden_size", [16])
@@ -101,6 +222,14 @@ def ops_in_model_after(self):
                     reason="Only test on CUDA")
 def test_async_tp_pass_replace(test_model: str, batch_size: int, seq_len: int,
                                hidden_size: int, dtype: torch.dtype):
+    if test_model in (TestScaledMMRSModel, TestAGScaledMMModel,
+                      TestCutlassScaledMMRSModel,
+                      TestAGCutlassScaledMMModel) and dtype == torch.float16:
+        pytest.skip(
+            "Only bf16 high precision output types are supported for " \
+            "per-token (row-wise) scaling"
+        )
+
     num_processes = 2
 
     def run_torch_spawn(fn, nprocs):
@@ -155,7 +284,8 @@ def async_tp_pass_on_test_model(local_rank: int, world_size: int,
     async_tp_pass = AsyncTPPass(vllm_config)
     backend = TestBackend(async_tp_pass)
 
-    model = test_model_cls(hidden_size)
+    model = test_model_cls(hidden_size,
+                           dtype)  # Pass dtype to model constructor
 
     hidden_states = torch.randn((batch_size * seq_len, hidden_size),
                                 dtype=dtype,
@@ -174,7 +304,10 @@ def async_tp_pass_on_test_model(local_rank: int, world_size: int,
 
 
 @create_new_process_for_each_test()
-@pytest.mark.parametrize("model_id", ["meta-llama/Llama-3.2-1B-Instruct"])
+@pytest.mark.parametrize("model_id", [
+    "meta-llama/Llama-3.2-1B-Instruct",
+    "RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8"
+])
 @pytest.mark.parametrize("tp_size", [2])
 @pytest.mark.parametrize("async_tp_enabled", [True])
 @pytest.mark.parametrize("distributed_backend", ["mp"])