[Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV by prsabahrami · Pull Request #6231 · modular/modular

prsabahrami · 2026-03-19T20:57:46Z

[Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV

BEGIN_PUBLIC
[Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV

When M=1, N is WARP_SIZE-aligned, but K is not, the GEMV dispatch
previously fell back to the naive matmul kernel. Add a bounds-checked
variant of the GEVM kernel (via comptime check_k_bounds parameter)
to handle this case efficiently, avoiding the ~20x performance cliff
between e.g. K=4096 and K=4095.
END_PUBLIC

Signed-off-by: PRAGMA Agent pragma-agent@modular.com

prsabahrami · 2026-03-19T20:57:49Z

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github-actions · 2026-03-19T20:57:56Z

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Pull request overview

This PR improves GEMV GPU dispatch for the M=1, N%WARP_SIZE==0, K%WARP_SIZE!=0 case by using a bounds-checked GEVM kernel variant instead of falling back to the naive matmul kernel, avoiding a major performance cliff for near-aligned K.

Changes:

Added a check_k_bounds compile-time parameter to gevm_kernel and guarded K-iteration loads when enabled.
Updated gemv_gpu dispatch to launch the bounds-checked GEVM kernel when M=1 and N is warp-aligned but K is not, instead of selecting the naive matmul path.

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Copilot · 2026-03-19T21:05:47Z

max/kernels/src/linalg/gemv.mojo

+            c.type,
+            a.type,
+            b.type,


In this new GEVM fast-path, c.type/a.type/b.type don’t appear to exist on TileTensor (it exposes dtype). This will fail to compile when specializing gevm_kernel[...]. Use the tensors’ dtypes (e.g., c.dtype, a.dtype, b.dtype) consistent with the rest of this file (see gemv_gpu_dispatch).

Suggested change

c.type,

a.type,

b.type,

c.dtype,

a.dtype,

b.dtype,

Copilot · 2026-03-19T21:05:48Z

max/kernels/src/linalg/gemv.mojo

+        var c_tensor = from_ndbuffer_row_major(c)
+        var a_tensor = from_ndbuffer_row_major(a)
+        var b_tensor = from_ndbuffer_row_major(b)
+        ctx.enqueue_function[kernel, kernel](
+            c_tensor.to_device_buffer(ctx),
+            a_tensor.to_device_buffer(ctx),
+            b_tensor.to_device_buffer(ctx),


from_ndbuffer_row_major is used here but isn’t imported into this module (and layout.__init__ doesn’t re-export it). Also, from_ndbuffer_row_major is defined for NDBuffer, while gemv_gpu takes TileTensor arguments, so these calls are likely a type mismatch even if imported. Consider using c.to_layout_tensor() / a.to_layout_tensor() / b.to_layout_tensor() (as gemv_gpu_dispatch does) or passing c.to_device_buffer(ctx) directly since gevm_kernel takes raw pointers.

Suggested change

var c_tensor = from_ndbuffer_row_major(c)

var a_tensor = from_ndbuffer_row_major(a)

var b_tensor = from_ndbuffer_row_major(b)

ctx.enqueue_function[kernel, kernel](

c_tensor.to_device_buffer(ctx),

a_tensor.to_device_buffer(ctx),

b_tensor.to_device_buffer(ctx),

ctx.enqueue_function[kernel, kernel](

c.to_device_buffer(ctx),

a.to_device_buffer(ctx),

b.to_device_buffer(ctx),

Copilot · 2026-03-19T21:05:48Z

max/kernels/src/linalg/gemv.mojo

    elif m == 1 and n % WARP_SIZE == 0 and k % WARP_SIZE == 0:
        kernel_func = GEMVAlgorithm.GEVM_KERNEL

+    elif m == 1 and n % WARP_SIZE == 0:
+        # K is not aligned to WARP_SIZE; use GEVM_KERNEL with bounds checking.
+        comptime WARPS_PER_BLOCK_LOCAL = 1024 // WARP_SIZE
+        comptime kernel = gevm_kernel[
+            c.type,
+            a.type,
+            b.type,
+            tile_size = WARP_SIZE * WARPS_PER_BLOCK_LOCAL,
+            check_k_bounds=True,
+            elementwise_lambda_fn=elementwise_lambda_fn,
+            pdl_level=pdl_level,
+        ]
+        var c_tensor = from_ndbuffer_row_major(c)
+        var a_tensor = from_ndbuffer_row_major(a)
+        var b_tensor = from_ndbuffer_row_major(b)
+        ctx.enqueue_function[kernel, kernel](
+            c_tensor.to_device_buffer(ctx),
+            a_tensor.to_device_buffer(ctx),
+            b_tensor.to_device_buffer(ctx),
+            m,
+            n,
+            k,
+            grid_dim=ceildiv(n, WARPS_PER_BLOCK_LOCAL),
+            block_dim=WARP_SIZE * WARPS_PER_BLOCK_LOCAL,
+            attributes=pdl_launch_attributes(pdl_level),
+        )
+        return
+


This branch duplicates the GEVM enqueue logic that already exists in gemv_gpu_dispatch (GEVM_KERNEL case). Duplicated launch setup (kernel specialization, buffer conversions, grid/block dims) increases the chance of future drift. A more maintainable approach is to route through gemv_gpu_dispatch by introducing a dedicated algorithm/flag for the bounds-checked GEVM variant, or by extending the existing GEVM dispatch to accept check_k_bounds.

Suggested change

elif m == 1 and n % WARP_SIZE == 0 and k % WARP_SIZE == 0:

kernel_func = GEMVAlgorithm.GEVM_KERNEL

elif m == 1 and n % WARP_SIZE == 0:

# K is not aligned to WARP_SIZE; use GEVM_KERNEL with bounds checking.

comptime WARPS_PER_BLOCK_LOCAL = 1024 // WARP_SIZE

comptime kernel = gevm_kernel[

c.type,

a.type,

b.type,

tile_size = WARP_SIZE * WARPS_PER_BLOCK_LOCAL,

check_k_bounds=True,

elementwise_lambda_fn=elementwise_lambda_fn,

pdl_level=pdl_level,

]

var c_tensor = from_ndbuffer_row_major(c)

var a_tensor = from_ndbuffer_row_major(a)

var b_tensor = from_ndbuffer_row_major(b)

ctx.enqueue_function[kernel, kernel](

c_tensor.to_device_buffer(ctx),

a_tensor.to_device_buffer(ctx),

b_tensor.to_device_buffer(ctx),

m,

n,

k,

grid_dim=ceildiv(n, WARPS_PER_BLOCK_LOCAL),

block_dim=WARP_SIZE * WARPS_PER_BLOCK_LOCAL,

attributes=pdl_launch_attributes(pdl_level),

)

return

elif m == 1 and n % WARP_SIZE == 0:

# Use the GEVM kernel for GEVM-style (m == 1) GEMV when n is warp-aligned.

# The common GEMV GPU dispatch will handle kernel launch details.

kernel_func = GEMVAlgorithm.GEVM_KERNEL

BEGIN_PUBLIC [Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV When M=1, N is WARP_SIZE-aligned, but K is not, the GEMV dispatch previously fell back to the naive matmul kernel. Add a bounds-checked variant of the GEVM kernel (via comptime check_k_bounds parameter) to handle this case efficiently, avoiding the ~20x performance cliff between e.g. K=4096 and K=4095. END_PUBLIC Signed-off-by: PRAGMA Agent <pragma-agent@modular.com>

prsabahrami · 2026-03-20T14:44:39Z

Closing per policy: draft PRs must not be opened without prior approval. Will re-open after getting explicit approval.

JoeLoser · 2026-03-20T16:00:19Z

Closing per policy: draft PRs must not be opened without prior approval. Will re-open after getting explicit approval.

Hi @prsabahrami, there's no issue/need for prior approval to open draft PRs. I chatted with the kernel team this morning and they'll check out the PRs from you the past day. No need for an explicit approval.

Appreciate your contributions, we'll get back to you soon! I'll reopen this in the meantime 🚀

govindansriram · 2026-03-23T07:34:04Z

max/kernels/gemv/profiling_config.yaml

@@ -0,0 +1,25 @@
+# Nsight Compute Profiling Configuration


I don't think we accept profiling configs, any particular reason why this is needed?

prsabahrami requested a review from a team as a code owner March 19, 2026 20:57

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prsabahrami marked this pull request as draft March 20, 2026 12:38

prsabahrami force-pushed the speedtrain/pragma-use-gevm-kernel-for-non-warpsize-aligned-k-in-gemv branch from e6d8e7c to a30cf97 Compare March 20, 2026 12:52

prsabahrami closed this Mar 20, 2026

JoeLoser reopened this Mar 20, 2026

govindansriram reviewed Mar 23, 2026

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[Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV#6231

[Kernels] Use GEVM kernel for non-WARP_SIZE-aligned K in GEMV#6231
prsabahrami wants to merge 1 commit intomodular:mainfrom
prsabahrami:speedtrain/pragma-use-gevm-kernel-for-non-warpsize-aligned-k-in-gemv

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-            c.type,
-            a.type,
-            b.type,
+            c.dtype,
+            a.dtype,
+            b.dtype,

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