[DO NOT MERGE] Experiments related to MoE kernels

Signed-off-by: Zhuohan Li <zhuohan123@gmail.com>

vllm/model_executor/layers/moe/grouped_gemm_no_abstraction.py

@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import os
+
+os.environ["VLLM_USE_DEEP_GEMM"] = "1"
+
+import math
+import random
+
+import torch
+
+from vllm.utils import deep_gemm as vllm_deep_gemm
+
+BLOCK_SIZE = (128, 128)
+BLOCK_N, BLOCK_K = BLOCK_SIZE
+
+
+def generate_bf16_and_downcast_to_fp8(shape, device="cuda"):
+    bf16_weight = torch.randn(shape, dtype=torch.bfloat16, device=device)
+    fp8_weight = bf16_weight.to(dtype=torch.float8_e4m3fn)
+    return fp8_weight
+
+
+def run_batched_deepgemm_fp8(
+    expected_group_batch_size: int,
+    num_groups: int,
+    output_size: int,
+    input_size: int,
+):
+    weight = generate_bf16_and_downcast_to_fp8((num_groups, output_size, input_size))
+    output_tiles = math.ceil(output_size / BLOCK_N)
+    input_tiles = math.ceil(input_size / BLOCK_K)
+    weight_scale = torch.randn(
+        num_groups,
+        output_tiles,
+        input_tiles,
+        dtype=torch.float32,
+        device="cuda",
+    )
+    group_batch_size = [
+        int(expected_group_batch_size * random.uniform(0.7, 1.3))
+        for _ in range(num_groups)
+    ]
+    batch_size = sum(group_batch_size)
+    group_batch_size = torch.tensor(
+        group_batch_size,
+        dtype=torch.int32,
+        device="cuda",
+    )
+    x = generate_bf16_and_downcast_to_fp8((num_groups, batch_size, input_size))
+    x_scale = torch.randn(
+        num_groups,
+        batch_size,
+        input_tiles,
+        dtype=torch.float32,
+        device="cuda",
+    )
+    output = torch.zeros(
+        num_groups,
+        batch_size,
+        output_size,
+        dtype=torch.bfloat16,
+        device="cuda",
+    )
+
+    vllm_deep_gemm.fp8_m_grouped_gemm_nt_masked(
+        (x, x_scale),
+        (weight, weight_scale),
+        output,
+        group_batch_size,
+        expected_group_batch_size,
+    )
+    print(output)
+
+
+def run_batched_deepgemm_bf16(
+    expected_group_batch_size: int,
+    num_groups: int,
+    output_size: int,
+    input_size: int,
+):
+    weight = torch.randn(
+        num_groups,
+        output_size,
+        input_size,
+        dtype=torch.bfloat16,
+        device="cuda",
+    )
+    group_batch_size = [
+        int(expected_group_batch_size * random.uniform(0.7, 1.3))
+        for _ in range(num_groups)
+    ]
+    batch_size = sum(group_batch_size)
+    group_batch_size = torch.tensor(
+        group_batch_size,
+        dtype=torch.int32,
+        device="cuda",
+    )
+    x = torch.randn(
+        num_groups,
+        batch_size,
+        input_size,
+        dtype=torch.bfloat16,
+        device="cuda",
+    )
+    ground_truth_output = torch.einsum("bnk, bmk -> bnm", x, weight)
+    output = torch.zeros(
+        num_groups,
+        batch_size,
+        output_size,
+        dtype=torch.bfloat16,
+        device="cuda",
+    )
+    vllm_deep_gemm.bf16_m_grouped_gemm_nt_masked(
+        x,
+        weight,
+        output,
+        group_batch_size,
+        expected_group_batch_size,
+    )
+    for i in range(num_groups):
+        torch.testing.assert_close(
+            output[i, : group_batch_size[i]],
+            ground_truth_output[i, : group_batch_size[i]],
+        )
+        print(
+            (
+                output[i, : group_batch_size[i]]
+                - ground_truth_output[i, : group_batch_size[i]]
+            )
+            .abs()
+            .max()
+        )
+
+
+run_batched_deepgemm_fp8(512, 8, 1024, 512)
+run_batched_deepgemm_bf16(512, 8, 1024, 512)

vllm/utils/deep_gemm.py

@@ -69,28 +69,37 @@ def _missing(*_: Any, **__: Any) -> NoReturn:
 
 
 _fp8_gemm_nt_impl: Callable[..., Any] | None = None
-_grouped_impl: Callable[..., Any] | None = None
-_grouped_masked_impl: Callable[..., Any] | None = None
+_fp8_grouped_impl: Callable[..., Any] | None = None
+_fp8_grouped_masked_impl: Callable[..., Any] | None = None
 _fp8_mqa_logits_impl: Callable[..., Any] | None = None
 _fp8_paged_mqa_logits_impl: Callable[..., Any] | None = None
+_bf16_grouped_impl: Callable[..., Any] | None = None
+_bf16_grouped_masked_impl: Callable[..., Any] | None = None
 _get_paged_mqa_logits_metadata_impl: Callable[..., Any] | None = None
 _get_mn_major_tma_aligned_tensor_impl: Callable[..., Any] | None = None
 
 
 def _lazy_init() -> None:
     """Import deep_gemm and resolve symbols on first use."""
-    global _fp8_gemm_nt_impl, _grouped_impl, _grouped_masked_impl
-    global _fp8_mqa_logits_impl, _fp8_paged_mqa_logits_impl
+    global _fp8_gemm_nt_impl
+    global _fp8_grouped_impl
+    global _fp8_grouped_masked_impl
+    global _fp8_mqa_logits_impl
+    global _fp8_paged_mqa_logits_impl
+    global _bf16_grouped_impl
+    global _bf16_grouped_masked_impl
     global _get_paged_mqa_logits_metadata_impl
     global _get_mn_major_tma_aligned_tensor_impl
 
     # fast path
     if (
         _fp8_gemm_nt_impl is not None
-        or _grouped_impl is not None
-        or _grouped_masked_impl is not None
+        or _fp8_grouped_impl is not None
+        or _fp8_grouped_masked_impl is not None
         or _fp8_mqa_logits_impl is not None
         or _fp8_paged_mqa_logits_impl is not None
+        or _bf16_grouped_impl is not None
+        or _bf16_grouped_masked_impl is not None
         or _get_paged_mqa_logits_metadata_impl is not None
     ):
         return
@@ -108,8 +117,10 @@ def _lazy_init() -> None:
     _dg = importlib.import_module("deep_gemm")
 
     _fp8_gemm_nt_impl = getattr(_dg, "fp8_gemm_nt", None)
-    _grouped_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_contiguous", None)
-    _grouped_masked_impl = getattr(_dg, "fp8_m_grouped_gemm_nt_masked", None)
+    _fp8_grouped_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_contiguous", None)
+    _fp8_grouped_masked_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_masked", None)
+    _bf16_grouped_impl = getattr(_dg, "m_grouped_bf16_gemm_nt_contiguous", None)
+    _bf16_grouped_masked_impl = getattr(_dg, "m_grouped_bf16_gemm_nt_masked", None)
     _fp8_mqa_logits_impl = getattr(_dg, "fp8_mqa_logits", None)
     _fp8_paged_mqa_logits_impl = getattr(_dg, "fp8_paged_mqa_logits", None)
     _get_paged_mqa_logits_metadata_impl = getattr(
@@ -148,22 +159,36 @@ def fp8_gemm_nt(*args, **kwargs):
 
 def m_grouped_fp8_gemm_nt_contiguous(*args, **kwargs):
     _lazy_init()
-    if _grouped_impl is None:
+    if _fp8_grouped_impl is None:
         return _missing(*args, **kwargs)
-    return _grouped_impl(
+    return _fp8_grouped_impl(
         *args, disable_ue8m0_cast=not is_deep_gemm_e8m0_used(), **kwargs
     )
 
 
 def fp8_m_grouped_gemm_nt_masked(*args, **kwargs):
     _lazy_init()
-    if _grouped_masked_impl is None:
+    if _fp8_grouped_masked_impl is None:
         return _missing(*args, **kwargs)
-    return _grouped_masked_impl(
+    return _fp8_grouped_masked_impl(
         *args, disable_ue8m0_cast=not is_deep_gemm_e8m0_used(), **kwargs
     )
 
 
+def m_grouped_bf16_gemm_nt_contiguous(*args, **kwargs):
+    _lazy_init()
+    if _bf16_grouped_impl is None:
+        return _missing(*args, **kwargs)
+    return _bf16_grouped_impl(*args, **kwargs)
+
+
+def bf16_m_grouped_gemm_nt_masked(*args, **kwargs):
+    _lazy_init()
+    if _bf16_grouped_masked_impl is None:
+        return _missing(*args, **kwargs)
+    return _bf16_grouped_masked_impl(*args, **kwargs)
+
+
 def fp8_mqa_logits(
     q: torch.Tensor,
     kv: tuple[torch.Tensor, torch.Tensor],