From 30c36b97f4bc1adbf23b1aea70c9469d34c0dc8b Mon Sep 17 00:00:00 2001
From: YiyanZhai <yiyanz@andrew.cmu.edu>
Date: Fri, 10 Oct 2025 01:44:34 -0400
Subject: [PATCH 1/4] fp8_gemm def update and wl

---
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 definitions/gemm/gemm_fp8_n2112_k7168.json    |  16 ++-
 definitions/gemm/gemm_fp8_n3072_k1536.json    |  16 ++-
 definitions/gemm/gemm_fp8_n4096_k512.json     |  16 ++-
 definitions/gemm/gemm_fp8_n4608_k7168.json    |  16 ++-
 definitions/gemm/gemm_fp8_n7168_k2048.json    |  16 ++-
 definitions/gemm/gemm_fp8_n7168_k2304.json    |  16 ++-
 workloads/gemm/gemm_fp8_n2112_k7168.jsonl     | 112 ++++++++---------
 workloads/gemm/gemm_fp8_n3072_k1536.jsonl     | 112 ++++++++---------
 workloads/gemm/gemm_fp8_n4096_k512.jsonl      |  11 +-
 workloads/gemm/gemm_fp8_n4608_k7168.jsonl     | 112 ++++++++---------
 workloads/gemm/gemm_fp8_n7168_k2048.jsonl     | 112 ++++++++---------
 workloads/gemm/gemm_fp8_n7168_k2304.jsonl     | 113 +++++++++---------
 304 files changed, 1252 insertions(+), 292 deletions(-)
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diff --git a/workloads/gemm/gemm_fp8_n7168_k2048.jsonl b/workloads/gemm/gemm_fp8_n7168_k2048.jsonl
index d6aec212..c21ec9d0 100644
--- a/workloads/gemm/gemm_fp8_n7168_k2048.jsonl
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index 5599eb3d..f6479e17 100644
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From 9a2d3089f367208da59638d5c4bd2c33fbb025de Mon Sep 17 00:00:00 2001
From: YiyanZhai <yiyanz@andrew.cmu.edu>
Date: Fri, 10 Oct 2025 13:25:44 -0400
Subject: [PATCH 2/4] add references and tests for uniform block sizes

---
 definitions/gemm/gemm_fp8_n2112_k7168.json    |   8 +-
 definitions/gemm/gemm_fp8_n3072_k1536.json    |   8 +-
 definitions/gemm/gemm_fp8_n4096_k512.json     |   8 +-
 definitions/gemm/gemm_fp8_n4608_k7168.json    |   8 +-
 definitions/gemm/gemm_fp8_n7168_k2048.json    |   8 +-
 tests/references/test_gemm_fp8_n2112_k7168.py | 247 ++++++++++++++++++
 tests/references/test_gemm_fp8_n3072_k1536.py | 243 +++++++++++++++++
 7 files changed, 510 insertions(+), 20 deletions(-)
 create mode 100644 tests/references/test_gemm_fp8_n2112_k7168.py
 create mode 100644 tests/references/test_gemm_fp8_n3072_k1536.py

diff --git a/definitions/gemm/gemm_fp8_n2112_k7168.json b/definitions/gemm/gemm_fp8_n2112_k7168.json
index 0cd957db..f91cd779 100644
--- a/definitions/gemm/gemm_fp8_n2112_k7168.json
+++ b/definitions/gemm/gemm_fp8_n2112_k7168.json
@@ -23,12 +23,12 @@
     "K-block-A": {
       "type": "const",
       "value": 56,
-      "description": "Number of columns in each block of A for scale factors."
+      "description": "Number of blocks along K for A's scales. Each block has size 128."
     },
     "K-block-B": {
       "type": "const",
       "value": 14,
-      "description": "Number of columns in each block of B for scale factors."
+      "description": "Number of blocks along K for B's scales. Each block has size 512."
     }
   },
   "inputs": {
@@ -58,7 +58,7 @@
         "N",
         "K-block-B"
       ],
-      "dtype": "float32"
+      "dtype": "int32"
     }
   },
   "outputs": {
@@ -70,5 +70,5 @@
       "dtype": "bfloat16"
     }
   },
-  "reference": "import torch\n\ndef run(A, B, A_scale, B_scale):\n    A_deq = A.to(torch.float32) * A_scale.view(-1, 1)\n    B_deq = B.to(torch.float32) * B_scale.view(-1, 1)\n    C = torch.matmul(A_deq, B_deq.T)\n    return C.to(torch.bfloat16)"
+  "reference": "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
 }
diff --git a/definitions/gemm/gemm_fp8_n3072_k1536.json b/definitions/gemm/gemm_fp8_n3072_k1536.json
index 205f949e..ef2f1e83 100644
--- a/definitions/gemm/gemm_fp8_n3072_k1536.json
+++ b/definitions/gemm/gemm_fp8_n3072_k1536.json
@@ -23,12 +23,12 @@
     "K-block-A": {
       "type": "const",
       "value": 12,
-      "description": "Number of columns in each block of A for scale factors."
+      "description": "Number of blocks along K for A's scales. Each block has size 128."
     },
     "K-block-B": {
       "type": "const",
       "value": 3,
-      "description": "Number of columns in each block of B for scale factors."
+      "description": "Number of blocks along K for B's scales. Each block has size 512."
     }
   },
   "inputs": {
@@ -58,7 +58,7 @@
         "N",
         "K-block-B"
       ],
-      "dtype": "float32"
+      "dtype": "int32"
     }
   },
   "outputs": {
@@ -70,5 +70,5 @@
       "dtype": "bfloat16"
     }
   },
-  "reference": "import torch\n\ndef run(A, B, A_scale, B_scale):\n    A_deq = A.to(torch.float32) * A_scale.view(-1, 1)\n    B_deq = B.to(torch.float32) * B_scale.view(-1, 1)\n    C = torch.matmul(A_deq, B_deq.T)\n    return C.to(torch.bfloat16)"
+  "reference": "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
 }
diff --git a/definitions/gemm/gemm_fp8_n4096_k512.json b/definitions/gemm/gemm_fp8_n4096_k512.json
index 8c487745..dc44ed6a 100644
--- a/definitions/gemm/gemm_fp8_n4096_k512.json
+++ b/definitions/gemm/gemm_fp8_n4096_k512.json
@@ -23,12 +23,12 @@
     "K-block-A": {
       "type": "const",
       "value": 4,
-      "description": "Number of columns in each block of A for scale factors."
+      "description": "Number of blocks along K for scale factors of A. Each block has size 128."
     },
     "K-block-B": {
       "type": "const",
       "value": 1,
-      "description": "Number of columns in each block of B for scale factors."
+      "description": "Number of blocks along K for scale factors of B. Each block has size 512."
     }
   },
   "inputs": {
@@ -58,7 +58,7 @@
         "N",
         "K-block-B"
       ],
-      "dtype": "float32"
+      "dtype": "int32"
     }
   },
   "outputs": {
@@ -70,5 +70,5 @@
       "dtype": "bfloat16"
     }
   },
-  "reference": "import torch\n\ndef run(A, B, A_scale, B_scale):\n    A_deq = A.to(torch.float32) * A_scale.view(-1, 1)\n    B_deq = B.to(torch.float32) * B_scale.view(-1, 1)\n    C = torch.matmul(A_deq, B_deq.T)\n    return C.to(torch.bfloat16)"
+  "reference": "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
 }
diff --git a/definitions/gemm/gemm_fp8_n4608_k7168.json b/definitions/gemm/gemm_fp8_n4608_k7168.json
index 69c5f8c2..02aafe41 100644
--- a/definitions/gemm/gemm_fp8_n4608_k7168.json
+++ b/definitions/gemm/gemm_fp8_n4608_k7168.json
@@ -23,12 +23,12 @@
     "K-block-A": {
       "type": "const",
       "value": 56,
-      "description": "Number of columns in each block of A for scale factors."
+      "description": "Number of blocks along K for A's scales. Each block has size 128."
     },
     "K-block-B": {
       "type": "const",
       "value": 14,
-      "description": "Number of columns in each block of B for scale factors."
+      "description": "Number of blocks along K for B's scales. Each block has size 512."
     }
   },
   "inputs": {
@@ -58,7 +58,7 @@
         "N",
         "K-block-B"
       ],
-      "dtype": "float32"
+      "dtype": "int32"
     }
   },
   "outputs": {
@@ -70,5 +70,5 @@
       "dtype": "bfloat16"
     }
   },
-  "reference": "import torch\n\ndef run(A, B, A_scale, B_scale):\n    A_deq = A.to(torch.float32) * A_scale.view(-1, 1)\n    B_deq = B.to(torch.float32) * B_scale.view(-1, 1)\n    C = torch.matmul(A_deq, B_deq.T)\n    return C.to(torch.bfloat16)"
+  "reference": "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
 }
diff --git a/definitions/gemm/gemm_fp8_n7168_k2048.json b/definitions/gemm/gemm_fp8_n7168_k2048.json
index dbd77478..efa8b8fa 100644
--- a/definitions/gemm/gemm_fp8_n7168_k2048.json
+++ b/definitions/gemm/gemm_fp8_n7168_k2048.json
@@ -23,12 +23,12 @@
     "K-block-A": {
       "type": "const",
       "value": 16,
-      "description": "Number of columns in each block of A for scale factors."
+      "description": "Number of blocks along K for A's scales. Each block has size 128."
     },
     "K-block-B": {
       "type": "const",
       "value": 4,
-      "description": "Number of columns in each block of B for scale factors."
+      "description": "Number of blocks along K for B's scales. Each block has size 512."
     }
   },
   "inputs": {
@@ -58,7 +58,7 @@
         "N",
         "K-block-B"
       ],
-      "dtype": "float32"
+      "dtype": "int32"
     }
   },
   "outputs": {
@@ -70,5 +70,5 @@
       "dtype": "bfloat16"
     }
   },
-  "reference": "import torch\n\ndef run(A, B, A_scale, B_scale):\n    A_deq = A.to(torch.float32) * A_scale.view(-1, 1)\n    B_deq = B.to(torch.float32) * B_scale.view(-1, 1)\n    C = torch.matmul(A_deq, B_deq.T)\n    return C.to(torch.bfloat16)"
+  "reference": "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
 }
diff --git a/tests/references/test_gemm_fp8_n2112_k7168.py b/tests/references/test_gemm_fp8_n2112_k7168.py
new file mode 100644
index 00000000..3e06f125
--- /dev/null
+++ b/tests/references/test_gemm_fp8_n2112_k7168.py
@@ -0,0 +1,247 @@
+import math
+import pytest
+import torch
+
+
+# ------------------------------------------------------------
+# Reference implementation
+# ------------------------------------------------------------
+
+
+reference_gemm_fp8_n2112_k7168_str = "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
+
+@torch.no_grad()
+def reference_gemm_fp8_n2112_k7168(A, B, A_scale, B_scale):
+    # Run the reference implementation from string
+    namespace = {}
+    exec(reference_gemm_fp8_n2112_k7168_str, namespace)
+    return namespace["run"](A, B, A_scale, B_scale)
+
+# ------------------------------------------------------------
+# FP8 block quant helpers (dequant semantics: real ≈ fp8 * scale)
+# ------------------------------------------------------------
+@torch.no_grad()
+def _fp8_block_quant_A(a_bf16: torch.Tensor, blockA: int = 128):
+    """
+    A: [M, K], per-(row, K-block) scales.
+    Returns:
+      A_fp8: [M, K] float8_e4m3fn
+      A_scale: [M, K/blk] float32
+    """
+    assert a_bf16.dim() == 2
+    M, K = a_bf16.shape
+    assert K % blockA == 0
+    Kb = K // blockA
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+
+    a_f32 = a_bf16.to(torch.float32)
+    a_fp8 = torch.empty((M, K), dtype=torch.float8_e4m3fn, device=a_bf16.device)
+    s = torch.empty((M, Kb), dtype=torch.float32, device=a_bf16.device)
+
+    for kb in range(Kb):
+        sl = slice(kb * blockA, (kb + 1) * blockA)
+        blk = a_f32[:, sl]                        # [M, blockA]
+        amax = torch.amax(torch.abs(blk), dim=1)  # [M]
+        scale = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        q = (blk / scale.unsqueeze(1)).to(torch.float8_e4m3fn)
+        a_fp8[:, sl] = q
+        s[:, kb] = scale
+    return a_fp8, s
+
+
+@torch.no_grad()
+def _fp8_block_quant_B(b_bf16: torch.Tensor, blockB: int = 512):
+    """
+    B: [N, K], per-(col, K-block) scales.
+    Returns:
+      B_fp8: [N, K] float8_e4m3fn
+      B_scale: [N, K/blk] float32
+    """
+    assert b_bf16.dim() == 2
+    N, K = b_bf16.shape
+    assert K % blockB == 0
+    Kb = K // blockB
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+
+    b_f32 = b_bf16.to(torch.float32)
+    b_fp8 = torch.empty((N, K), dtype=torch.float8_e4m3fn, device=b_bf16.device)
+    s = torch.empty((N, Kb), dtype=torch.int32, device=b_bf16.device)
+
+    for kb in range(Kb):
+        sl = slice(kb * blockB, (kb + 1) * blockB)
+        blk = b_f32[:, sl]                        # [N, blockB]
+        amax = torch.amax(torch.abs(blk), dim=1)  # [N]
+        scale = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        q = (blk / scale.unsqueeze(1)).to(torch.float8_e4m3fn)
+        b_fp8[:, sl] = q
+        s[:, kb] = scale
+    return b_fp8, s
+
+
+# ------------------------------------------------------------
+# Optional kernel under test (if available)
+# ------------------------------------------------------------
+try:
+    # Adjust this import/name to your actual kernel symbol if different.
+    from flashinfer.gemm import gemm_fp8_nt_groupwise as _kernel_gemm
+except Exception:
+    _kernel_gemm = None
+
+
+# ------------------------------------------------------------
+# Input generator for the specific op: N=2112, K=7168
+# ------------------------------------------------------------
+@torch.no_grad()
+def generate_inputs_fp8_gemm(
+    M: int,
+    *,
+    N: int = 2112,
+    K: int = 7168,
+    blockA: int = 128,   # -> KbA=56
+    blockB: int = 512,   # -> KbB=14
+    device: str = "cuda",
+):
+    assert K % blockA == 0 and K % blockB == 0
+    KbA = K // blockA
+    KbB = K // blockB
+
+    # Start from bf16 "real" values
+    A_bf16 = 1.5 * torch.randn(M, K, dtype=torch.bfloat16, device=device)
+    B_bf16 = 1.5 * torch.randn(N, K, dtype=torch.bfloat16, device=device)
+
+    # Quantize to FP8 with per-block scales
+    A_fp8, A_scale = _fp8_block_quant_A(A_bf16, blockA=blockA)   # [M,K], [M,KbA]
+    B_fp8, B_scale = _fp8_block_quant_B(B_bf16, blockB=blockB)   # [N,K], [N,KbB]
+
+    # Sanity shapes
+    assert A_fp8.shape == (M, K)
+    assert B_fp8.shape == (N, K)
+    assert A_scale.shape == (M, KbA)
+    assert B_scale.shape == (N, KbB)
+
+    return dict(A=A_fp8, B=B_fp8, A_scale=A_scale, B_scale=B_scale)
+
+
+# ------------------------------------------------------------
+# Comparator (reference vs kernel if present)
+# ------------------------------------------------------------
+def _compare_reference_vs_kernel(inputs, *, atol=1e-1, rtol=2e-1, percent=0.98):
+    print("Running reference...")
+    ref = reference_gemm_fp8_n2112_k7168(
+        inputs["A"], inputs["B"], inputs["A_scale"], inputs["B_scale"]
+    ).float()
+
+    if _kernel_gemm is None:
+        print("WARNING: kernel not available; only ran reference path.")
+        # Minimal numerical self-consistency check vs itself
+        ok = True
+        cos_sim = 1.0
+        mse = 0.0
+        max_abs = 0.0
+        mean_abs = 0.0
+    else:
+        print("Running kernel...")
+        out = _kernel_gemm(
+            inputs["A"],          # [M,K] fp8
+            inputs["B"],          # [N,K] fp8
+            inputs["A_scale"],    # [M,56] f32
+            inputs["B_scale"],    # [N,14] f32
+            backend="trtllm",
+        ).float()
+
+        abs_diff = (ref - out).abs()
+        rel_diff = abs_diff / (out.abs() + 1e-8)
+
+        max_abs = abs_diff.max().item()
+        mean_abs = abs_diff.mean().item()
+        cos_sim = torch.nn.functional.cosine_similarity(ref.flatten(), out.flatten(), dim=0).item()
+        mse = torch.mean((ref - out) ** 2).item()
+
+        print("\nComparison stats:")
+        print(f"Max abs diff:  {max_abs:.6e}")
+        print(f"Mean abs diff: {mean_abs:.6e}")
+        print(f"Cosine sim:    {cos_sim:.6f}")
+        print(f"MSE:           {mse:.6e}")
+
+        # allclose + element-wise hit ratio
+        allclose = torch.allclose(ref, out, atol=atol, rtol=rtol)
+        print(f"Allclose(atol={atol}, rtol={rtol}): {allclose}")
+
+        left = abs_diff
+        right = atol + rtol * out.abs()
+        ok_mask = left <= right
+        hit_ratio = ok_mask.float().mean().item()
+        print(f"Hit ratio: {hit_ratio * 100:.2f}% (need >= {percent * 100:.2f}%)")
+        ok = hit_ratio >= percent
+
+        if not allclose:
+            # Print top-5 error indices
+            flat = abs_diff.flatten()
+            k = min(5, flat.numel())
+            topv, topi = torch.topk(flat, k)
+            Mdim, Ndim = ref.shape
+            print("\nTop-5 absolute error locations:")
+            for rank in range(k):
+                idx = topi[rank].item()
+                m = idx // Ndim
+                n = idx % Ndim
+                print(
+                    f"  [m={m}, n={n}]: ref={ref[m, n].item():.6e}, "
+                    f"ker={out[m, n].item():.6e}, diff={topv[rank].item():.6e}"
+                )
+
+    # Loose sanity checks on reference output distribution
+    assert ref.dtype == torch.float32
+    assert not torch.isnan(ref).any()
+    assert not torch.isinf(ref).any()
+    return ok
+
+
+# ------------------------------------------------------------
+# PyTest entry points
+# ------------------------------------------------------------
+def _cuda_or_skip():
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available; skipping FP8 GEMM test.")
+
+
+@pytest.mark.parametrize("M", [1, 4, 16, 64, 256, 1024])
+def test_fp8_gemm_reference_vs_kernel(M):
+    """
+    Tests the GEMM op with K=7168, N=2112 and
+    A-block=128 (KbA=56), B-block=512 (KbB=14).
+    """
+    _cuda_or_skip()
+
+    device = "cuda"
+    torch.manual_seed(1234)
+
+    inputs = generate_inputs_fp8_gemm(M, device=device)
+    ok = _compare_reference_vs_kernel(
+        inputs,
+        atol=1e-1,
+        rtol=2e-1,
+        percent=0.98,   # GEMM should be highly consistent
+    )
+    assert ok, "Kernel output mismatched reference beyond tolerance."
+
+
+def main():
+    print("Running ad-hoc FP8 GEMM test...")
+    M_list = [1, 4, 16, 64]
+    for M in M_list:
+        print(f"\n=== M={M} ===")
+        inputs = generate_inputs_fp8_gemm(M, device="cuda")
+        for k, v in inputs.items():
+            print(f"{k}: shape={tuple(v.shape)}, dtype={v.dtype}")
+                    
+        ok = _compare_reference_vs_kernel(inputs, percent=0.98)
+        print(f"Result: {'OK' if ok else 'Mismatch'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/references/test_gemm_fp8_n3072_k1536.py b/tests/references/test_gemm_fp8_n3072_k1536.py
new file mode 100644
index 00000000..a933664d
--- /dev/null
+++ b/tests/references/test_gemm_fp8_n3072_k1536.py
@@ -0,0 +1,243 @@
+import math
+import pytest
+import torch
+
+
+# ------------------------------------------------------------
+# Reference implementation
+# ------------------------------------------------------------
+
+
+reference_gemm_fp8_n3072_k1536_str = "import torch\n\n\ndef run(A, B, A_scale, B_scale):\n    \"\"\"\n    GEMM with FP8 inputs and per-block scales.\n\n    A: [M, K] float8_e4m3fn (row-major)\n    B: [N, K] float8_e4m3fn (column-major)\n    A_scale: [M, K-block-A] float32 (per-row, per-K-block)\n    B_scale: [N, K-block-B] int32 (per-col, per-K-block)\n    Returns: [M, N] bfloat16\n    \"\"\"\n\n    M, K = A.shape\n    N, _ = B.shape\n    KbA = A_scale.shape[1]   # 56\n    KbB = B_scale.shape[1]   # 14\n\n    blockA = K // KbA        # 128\n    blockB = K // KbB        # 512\n\n    # Convert FP8 -> float32\n    A_f32 = A.to(torch.float32)\n    B_f32 = B.to(torch.float32)\n\n    # Apply A scales: (M, K) -> (M, KbA, blockA)\n    A_view  = A_f32.view(M, KbA, blockA)\n    As_view = A_scale.to(torch.float32).unsqueeze(-1)\n    A_deq   = (A_view * As_view).reshape(M, K)\n\n    # Apply B scales: (N, K) -> (N, KbB, blockB)\n    B_view  = B_f32.view(N, KbB, blockB)\n    Bs_view = B_scale.to(torch.float32).unsqueeze(-1)\n    B_deq   = (B_view * Bs_view).reshape(N, K)\n\n    # GEMM: C = A @ B^T\n    C = torch.matmul(A_deq, B_deq.transpose(0, 1))\n    return C.to(torch.bfloat16)"
+
+@torch.no_grad()
+def reference_gemm_fp8_n3072_k1536(A, B, A_scale, B_scale):
+    # Run the reference implementation from string
+    namespace = {}
+    exec(reference_gemm_fp8_n3072_k1536_str, namespace)
+    return namespace["run"](A, B, A_scale, B_scale)
+
+# ------------------------------------------------------------
+# FP8 block quant helpers (dequant semantics: real ≈ fp8 * scale)
+# ------------------------------------------------------------
+@torch.no_grad()
+def _fp8_block_quant_A(a_bf16: torch.Tensor, blockA: int = 128):
+    """
+    A: [M, K], per-(row, K-block) scales.
+    Returns:
+      A_fp8: [M, K] float8_e4m3fn
+      A_scale: [M, K/blk] float32
+    """
+    assert a_bf16.dim() == 2
+    M, K = a_bf16.shape
+    assert K % blockA == 0
+    Kb = K // blockA
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+
+    a_f32 = a_bf16.to(torch.float32)
+    a_fp8 = torch.empty((M, K), dtype=torch.float8_e4m3fn, device=a_bf16.device)
+    s = torch.empty((M, Kb), dtype=torch.float32, device=a_bf16.device)
+
+    for kb in range(Kb):
+        sl = slice(kb * blockA, (kb + 1) * blockA)
+        blk = a_f32[:, sl]                        # [M, blockA]
+        amax = torch.amax(torch.abs(blk), dim=1)  # [M]
+        scale = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        q = (blk / scale.unsqueeze(1)).to(torch.float8_e4m3fn)
+        a_fp8[:, sl] = q
+        s[:, kb] = scale
+    return a_fp8, s
+
+
+@torch.no_grad()
+def _fp8_block_quant_B(b_bf16: torch.Tensor, blockB: int = 512):
+    """
+    B: [N, K], per-(col, K-block) scales.
+    Returns:
+      B_fp8: [N, K] float8_e4m3fn
+      B_scale: [N, K/blk] float32
+    """
+    assert b_bf16.dim() == 2
+    N, K = b_bf16.shape
+    assert K % blockB == 0
+    Kb = K // blockB
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+
+    b_f32 = b_bf16.to(torch.float32)
+    b_fp8 = torch.empty((N, K), dtype=torch.float8_e4m3fn, device=b_bf16.device)
+    s = torch.empty((N, Kb), dtype=torch.int32, device=b_bf16.device)
+
+    for kb in range(Kb):
+        sl = slice(kb * blockB, (kb + 1) * blockB)
+        blk = b_f32[:, sl]                        # [N, blockB]
+        amax = torch.amax(torch.abs(blk), dim=1)  # [N]
+        scale = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        q = (blk / scale.unsqueeze(1)).to(torch.float8_e4m3fn)
+        b_fp8[:, sl] = q
+        s[:, kb] = scale
+    return b_fp8, s
+
+
+# ------------------------------------------------------------
+# Optional kernel under test (if available)
+# ------------------------------------------------------------
+try:
+    # Adjust this import/name to your actual kernel symbol if different.
+    from flashinfer.gemm import gemm_fp8_nt_groupwise as _kernel_gemm
+except Exception:
+    _kernel_gemm = None
+
+
+# ------------------------------------------------------------
+# Input generator for the specific op: N=3072, K=1536
+# ------------------------------------------------------------
+@torch.no_grad()
+def generate_inputs_fp8_gemm(
+    M: int,
+    *,
+    N: int = 3072,
+    K: int = 1536,
+    blockA: int = 128,   # -> KbA=12
+    blockB: int = 512,   # -> KbB=3
+    device: str = "cuda",
+):
+    assert K % blockA == 0 and K % blockB == 0
+    KbA = K // blockA
+    KbB = K // blockB
+
+    # Start from bf16 "real" values
+    A_bf16 = 1.5 * torch.randn(M, K, dtype=torch.bfloat16, device=device)
+    B_bf16 = 1.5 * torch.randn(N, K, dtype=torch.bfloat16, device=device)
+
+    # Quantize to FP8 with per-block scales
+    A_fp8, A_scale = _fp8_block_quant_A(A_bf16, blockA=blockA)   # [M,K], [M,KbA]
+    B_fp8, B_scale = _fp8_block_quant_B(B_bf16, blockB=blockB)   # [N,K], [N,KbB]
+
+    # Sanity shapes
+    assert A_fp8.shape == (M, K)
+    assert B_fp8.shape == (N, K)
+    assert A_scale.shape == (M, KbA)
+    assert B_scale.shape == (N, KbB)
+
+    return dict(A=A_fp8, B=B_fp8, A_scale=A_scale, B_scale=B_scale)
+
+
+# ------------------------------------------------------------
+# Comparator (reference vs kernel if present)
+# ------------------------------------------------------------
+def _compare_reference_vs_kernel(inputs, *, atol=1e-1, rtol=2e-1, percent=0.98):
+    print("Running reference...")
+    ref = reference_gemm_fp8_n3072_k1536(
+        inputs["A"], inputs["B"], inputs["A_scale"], inputs["B_scale"]
+    ).float()
+
+    if _kernel_gemm is None:
+        print("WARNING: kernel not available; only ran reference path.")
+        # Minimal numerical self-consistency check vs itself
+        ok = True
+        cos_sim = 1.0
+        mse = 0.0
+        max_abs = 0.0
+        mean_abs = 0.0
+    else:
+        print("Running kernel...")
+        out = _kernel_gemm(
+            inputs["A"],          # [M,K] fp8
+            inputs["B"],          # [N,K] fp8
+            inputs["A_scale"],    # [M,56] f32
+            inputs["B_scale"],    # [N,14] f32
+            backend="trtllm",
+        ).float()
+
+        abs_diff = (ref - out).abs()
+        rel_diff = abs_diff / (out.abs() + 1e-8)
+
+        max_abs = abs_diff.max().item()
+        mean_abs = abs_diff.mean().item()
+        cos_sim = torch.nn.functional.cosine_similarity(ref.flatten(), out.flatten(), dim=0).item()
+        mse = torch.mean((ref - out) ** 2).item()
+
+        print("\nComparison stats:")
+        print(f"Max abs diff:  {max_abs:.6e}")
+        print(f"Mean abs diff: {mean_abs:.6e}")
+        print(f"Cosine sim:    {cos_sim:.6f}")
+        print(f"MSE:           {mse:.6e}")
+
+        # allclose + element-wise hit ratio
+        allclose = torch.allclose(ref, out, atol=atol, rtol=rtol)
+        print(f"Allclose(atol={atol}, rtol={rtol}): {allclose}")
+
+        left = abs_diff
+        right = atol + rtol * out.abs()
+        ok_mask = left <= right
+        hit_ratio = ok_mask.float().mean().item()
+        print(f"Hit ratio: {hit_ratio * 100:.2f}% (need >= {percent * 100:.2f}%)")
+        ok = hit_ratio >= percent
+
+        if not allclose:
+            # Print top-5 error indices
+            flat = abs_diff.flatten()
+            k = min(5, flat.numel())
+            topv, topi = torch.topk(flat, k)
+            Mdim, Ndim = ref.shape
+            print("\nTop-5 absolute error locations:")
+            for rank in range(k):
+                idx = topi[rank].item()
+                m = idx // Ndim
+                n = idx % Ndim
+                print(
+                    f"  [m={m}, n={n}]: ref={ref[m, n].item():.6e}, "
+                    f"ker={out[m, n].item():.6e}, diff={topv[rank].item():.6e}"
+                )
+
+    # Loose sanity checks on reference output distribution
+    assert ref.dtype == torch.float32
+    assert not torch.isnan(ref).any()
+    assert not torch.isinf(ref).any()
+    return ok
+
+
+# ------------------------------------------------------------
+# PyTest entry points
+# ------------------------------------------------------------
+def _cuda_or_skip():
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available; skipping FP8 GEMM test.")
+
+
+@pytest.mark.parametrize("M", [1, 4, 16, 64, 256, 1024])
+def test_fp8_gemm_reference_vs_kernel(M):
+    _cuda_or_skip()
+
+    device = "cuda"
+    torch.manual_seed(1234)
+
+    inputs = generate_inputs_fp8_gemm(M, device=device)
+    ok = _compare_reference_vs_kernel(
+        inputs,
+        atol=1e-1,
+        rtol=2e-1,
+        percent=0.98,   # GEMM should be highly consistent
+    )
+    assert ok, "Kernel output mismatched reference beyond tolerance."
+
+
+def main():
+    print("Running ad-hoc FP8 GEMM test...")
+    M_list = [1, 4, 16, 64]
+    for M in M_list:
+        print(f"\n=== M={M} ===")
+        inputs = generate_inputs_fp8_gemm(M, device="cuda")
+        for k, v in inputs.items():
+            print(f"{k}: shape={tuple(v.shape)}, dtype={v.dtype}")
+                    
+        ok = _compare_reference_vs_kernel(inputs, percent=0.98)
+        print(f"Result: {'OK' if ok else 'Mismatch'}")
+
+
+if __name__ == "__main__":
+    main()

From 734b5f4461d9ca92a9fd801cd8ab032730c7151b Mon Sep 17 00:00:00 2001
From: YiyanZhai <yiyanz@andrew.cmu.edu>
Date: Fri, 10 Oct 2025 13:49:50 -0400
Subject: [PATCH 3/4] debugging k=2304, num_blocks=5

---
 tests/references/test_gemm_fp8_n7168_k2304.py | 291 ++++++++++++++++++
 1 file changed, 291 insertions(+)
 create mode 100644 tests/references/test_gemm_fp8_n7168_k2304.py

diff --git a/tests/references/test_gemm_fp8_n7168_k2304.py b/tests/references/test_gemm_fp8_n7168_k2304.py
new file mode 100644
index 00000000..340d8d15
--- /dev/null
+++ b/tests/references/test_gemm_fp8_n7168_k2304.py
@@ -0,0 +1,291 @@
+import math
+import pytest
+import torch
+
+
+# ------------------------------------------------------------
+# Reference implementation
+# ------------------------------------------------------------
+
+import torch
+
+def _expand_scales_along_K_per_row(scales_row_kblk: torch.Tensor, K: int, block: int):
+    """
+    scales_row_kblk: [R, Kb] where Kb = ceil(K / block)
+    Returns expanded scales: [R, K] with last block possibly ragged.
+    """
+    R, Kb = scales_row_kblk.shape
+    sizes = [block] * (K // block)
+    rem = K % block
+    if rem:
+        sizes.append(rem)
+    print(f"R={R}, Kb={Kb}, K={K}, block={block}, sizes={sizes}")  # Debug print
+    assert len(sizes) == Kb, f"shape mismatch: Kb={Kb}, sizes={sizes}"
+
+    parts = []
+    for kb, sz in enumerate(sizes):
+        print(f"Expanding block {kb}: size={sz}")  # Debug print
+        # [R, 1] -> [R, sz]
+        part = scales_row_kblk[:, kb:kb+1].repeat(1, sz)
+        print(f"part.shape={part.shape}")  # Debug print
+        parts.append(part)
+    B_scales_expanded = torch.cat(parts, dim=1)  # [R, K]
+    print(f"B_scales_expanded.shape={B_scales_expanded.shape}")  # Debug print
+    return B_scales_expanded
+
+@torch.no_grad()
+def reference_gemm_fp8_n7168_k2304(A, B, A_scale, B_scale):
+    """
+    A: [M, K] fp8_e4m3fn, per-(row, 128) scales  A_scale: [M, ceil(K/128)]
+    B: [N, K] fp8_e4m3fn, per-(col, 512) scales  B_scale: [N, ceil(K/512)]
+    Output: [M, N] bf16
+    """
+    M, K = A.shape
+    N, K2 = B.shape
+    assert K == K2
+
+    # A (uniform 128 blocks → can use view)
+    KbA = A_scale.shape[1]
+    blockA = 128
+    assert KbA == (K + blockA - 1) // blockA
+    A_f32 = A.to(torch.float32)
+    A_view  = A_f32.view(M, KbA, blockA)
+    print(f"A_view.shape={A_view.shape}")  # Debug print
+    As_view = A_scale.to(torch.float32).unsqueeze(-1)
+    print(f"As_view.shape={As_view.shape}")  # Debug print
+    A_deq   = (A_view * As_view)
+    print(f"A_deq.shape={A_deq.shape}")  # Debug print
+
+    # B (512 blocks → ragged last → expand with helper)
+    blockB = 512
+    KbB = B_scale.shape[1]
+    assert KbB == (K + blockB - 1) // blockB
+    B_f32 = B.to(torch.float32)
+    B_scales_expanded = _expand_scales_along_K_per_row(B_scale.to(torch.float32), K, block=blockB)  # [N, K]
+    B_deq = B_f32 * B_scales_expanded
+
+    C = torch.matmul(A_deq.reshape(M, K), B_deq.reshape(N, K).transpose(0, 1))
+    return C.to(torch.bfloat16)
+
+# ------------------------------------------------------------
+# FP8 block quant helpers (dequant semantics: real ≈ fp8 * scale)
+# ------------------------------------------------------------
+@torch.no_grad()
+def _fp8_block_quant_A(a_bf16: torch.Tensor, blockA: int = 128):
+    """
+    A: [M, K], per-(row, K-block) scales.
+    Returns:
+      A_fp8: [M, K] float8_e4m3fn
+      A_scale: [M, K/blk] float32
+    """
+    assert a_bf16.dim() == 2
+    M, K = a_bf16.shape
+    assert K % blockA == 0
+    Kb = K // blockA
+
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+
+    a_f32 = a_bf16.to(torch.float32)
+    a_fp8 = torch.empty((M, K), dtype=torch.float8_e4m3fn, device=a_bf16.device)
+    s = torch.empty((M, Kb), dtype=torch.float32, device=a_bf16.device)
+
+    for kb in range(Kb):
+        sl = slice(kb * blockA, (kb + 1) * blockA)
+        blk = a_f32[:, sl]                        # [M, blockA]
+        amax = torch.amax(torch.abs(blk), dim=1)  # [M]
+        scale = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        q = (blk / scale.unsqueeze(1)).to(torch.float8_e4m3fn)
+        a_fp8[:, sl] = q
+        s[:, kb] = scale
+    return a_fp8, s
+
+
+@torch.no_grad()
+def _fp8_block_quant_B(b_bf16: torch.Tensor, blockB: int = 512):
+    N, K = b_bf16.shape
+    finfo = torch.finfo(torch.float8_e4m3fn)
+    xmax = finfo.max
+    b_f32 = b_bf16.to(torch.float32)
+    b_fp8 = torch.empty((N, K), dtype=torch.float8_e4m3fn, device=b_bf16.device)
+
+    sizes = [blockB] * (K // blockB)
+    rem = K % blockB
+    if rem:
+        sizes.append(rem)
+    Kb = len(sizes)
+    scales = torch.empty((N, Kb), dtype=torch.float32, device=b_bf16.device)
+
+    k0 = 0
+    for kb, sz in enumerate(sizes):
+        sl = slice(k0, k0 + sz)
+        blk = b_f32[:, sl]                       # [N, sz]
+        amax = torch.amax(torch.abs(blk), dim=1) # [N]
+        s = torch.where(amax > 0, amax / xmax, torch.ones_like(amax))
+        b_fp8[:, sl] = (blk / s.unsqueeze(1)).to(torch.float8_e4m3fn)
+        scales[:, kb] = s
+        k0 += sz
+    return b_fp8, scales  # [N,K], [N,ceil(K/512)]
+
+# ------------------------------------------------------------
+# Optional kernel under test (if available)
+# ------------------------------------------------------------
+try:
+    # Adjust this import/name to your actual kernel symbol if different.
+    from flashinfer.gemm import gemm_fp8_nt_groupwise as _kernel_gemm
+except Exception:
+    _kernel_gemm = None
+
+
+# ------------------------------------------------------------
+# Input generator for the specific op: N=2112, K=7168
+# ------------------------------------------------------------
+@torch.no_grad()
+def generate_inputs_fp8_gemm(
+    M: int,
+    *,
+    N: int = 7168,
+    K: int = 2304,
+    blockA: int = 128,   # -> KbA=18
+    blockB: int = 512,   # -> KbB=5
+    device: str = "cuda",
+):
+    KbA = K // blockA
+    KbB = 5
+
+    # Start from bf16 "real" values
+    A_bf16 = 1.5 * torch.randn(M, K, dtype=torch.bfloat16, device=device)
+    B_bf16 = 1.5 * torch.randn(N, K, dtype=torch.bfloat16, device=device)
+
+    # Quantize to FP8 with per-block scales
+    A_fp8, A_scale = _fp8_block_quant_A(A_bf16, blockA=blockA)   # [M,K], [M,KbA]
+    B_fp8, B_scale = _fp8_block_quant_B(B_bf16, blockB=blockB)   # [N,K], [N,KbB]
+
+    # Sanity shapes
+    assert A_fp8.shape == (M, K)
+    assert B_fp8.shape == (N, K)
+    assert A_scale.shape == (M, KbA)
+    assert B_scale.shape == (N, KbB)
+
+    return dict(A=A_fp8, B=B_fp8, A_scale=A_scale, B_scale=B_scale)
+
+
+# ------------------------------------------------------------
+# Comparator (reference vs kernel if present)
+# ------------------------------------------------------------
+def _compare_reference_vs_kernel(inputs, *, atol=1e-1, rtol=2e-1, percent=0.98):
+    print("Running reference...")
+    ref = reference_gemm_fp8_n7168_k2304(
+        inputs["A"], inputs["B"], inputs["A_scale"], inputs["B_scale"]
+    ).float()
+
+    if _kernel_gemm is None:
+        print("WARNING: kernel not available; only ran reference path.")
+        # Minimal numerical self-consistency check vs itself
+        ok = True
+        cos_sim = 1.0
+        mse = 0.0
+        max_abs = 0.0
+        mean_abs = 0.0
+    else:
+        print("Running kernel...")
+        out = _kernel_gemm(
+            inputs["A"],          # [M,K] fp8
+            inputs["B"],          # [N,K] fp8
+            inputs["A_scale"],    # [M,56] f32
+            inputs["B_scale"],    # [N,14] f32
+            backend="trtllm",
+        ).float()
+
+        abs_diff = (ref - out).abs()
+        rel_diff = abs_diff / (out.abs() + 1e-8)
+
+        max_abs = abs_diff.max().item()
+        mean_abs = abs_diff.mean().item()
+        cos_sim = torch.nn.functional.cosine_similarity(ref.flatten(), out.flatten(), dim=0).item()
+        mse = torch.mean((ref - out) ** 2).item()
+
+        print("\nComparison stats:")
+        print(f"Max abs diff:  {max_abs:.6e}")
+        print(f"Mean abs diff: {mean_abs:.6e}")
+        print(f"Cosine sim:    {cos_sim:.6f}")
+        print(f"MSE:           {mse:.6e}")
+
+        # allclose + element-wise hit ratio
+        allclose = torch.allclose(ref, out, atol=atol, rtol=rtol)
+        print(f"Allclose(atol={atol}, rtol={rtol}): {allclose}")
+
+        left = abs_diff
+        right = atol + rtol * out.abs()
+        ok_mask = left <= right
+        hit_ratio = ok_mask.float().mean().item()
+        print(f"Hit ratio: {hit_ratio * 100:.2f}% (need >= {percent * 100:.2f}%)")
+        ok = hit_ratio >= percent
+
+        if not allclose:
+            # Print top-5 error indices
+            flat = abs_diff.flatten()
+            k = min(5, flat.numel())
+            topv, topi = torch.topk(flat, k)
+            Mdim, Ndim = ref.shape
+            print("\nTop-5 absolute error locations:")
+            for rank in range(k):
+                idx = topi[rank].item()
+                m = idx // Ndim
+                n = idx % Ndim
+                print(
+                    f"  [m={m}, n={n}]: ref={ref[m, n].item():.6e}, "
+                    f"ker={out[m, n].item():.6e}, diff={topv[rank].item():.6e}"
+                )
+
+    # Loose sanity checks on reference output distribution
+    assert ref.dtype == torch.float32
+    assert not torch.isnan(ref).any()
+    assert not torch.isinf(ref).any()
+    return ok
+
+
+# ------------------------------------------------------------
+# PyTest entry points
+# ------------------------------------------------------------
+def _cuda_or_skip():
+    if not torch.cuda.is_available():
+        pytest.skip("CUDA not available; skipping FP8 GEMM test.")
+
+
+@pytest.mark.parametrize("M", [1, 4, 16, 64, 256, 1024])
+def test_fp8_gemm_reference_vs_kernel(M):
+    """
+    Tests the GEMM op with K=7168, N=2112 and
+    A-block=128 (KbA=56), B-block=512 (KbB=14).
+    """
+    _cuda_or_skip()
+
+    device = "cuda"
+    torch.manual_seed(1234)
+
+    inputs = generate_inputs_fp8_gemm(M, device=device)
+    ok = _compare_reference_vs_kernel(
+        inputs,
+        atol=1e-1,
+        rtol=2e-1,
+        percent=0.98,   # GEMM should be highly consistent
+    )
+    assert ok, "Kernel output mismatched reference beyond tolerance."
+
+
+def main():
+    print("Running ad-hoc FP8 GEMM test...")
+    M_list = [1, 4, 16, 64]
+    for M in M_list:
+        print(f"\n=== M={M} ===")
+        inputs = generate_inputs_fp8_gemm(M, device="cuda")
+        for k, v in inputs.items():
+            print(f"{k}: shape={tuple(v.shape)}, dtype={v.dtype}")
+                    
+        ok = _compare_reference_vs_kernel(inputs, percent=0.98)
+        print(f"Result: {'OK' if ok else 'Mismatch'}")
+
+
+if __name__ == "__main__":
+    main()

From 81750bed903bdcca69c7889597f4ba2d6b73d05e Mon Sep 17 00:00:00 2001
From: YiyanZhai <yiyanz@andrew.cmu.edu>
Date: Mon, 13 Oct 2025 01:16:23 -0400
Subject: [PATCH 4/4] test case for generated inputs

---
 tests/references/test_gemm_fp8_n3072_k1536.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/references/test_gemm_fp8_n3072_k1536.py b/tests/references/test_gemm_fp8_n3072_k1536.py
index a933664d..e33b3bc4 100644
--- a/tests/references/test_gemm_fp8_n3072_k1536.py
+++ b/tests/references/test_gemm_fp8_n3072_k1536.py
@@ -69,7 +69,7 @@ def _fp8_block_quant_B(b_bf16: torch.Tensor, blockB: int = 512):
 
     b_f32 = b_bf16.to(torch.float32)
     b_fp8 = torch.empty((N, K), dtype=torch.float8_e4m3fn, device=b_bf16.device)
-    s = torch.empty((N, Kb), dtype=torch.int32, device=b_bf16.device)
+    s = torch.empty((N, Kb), dtype=torch.float32, device=b_bf16.device)
 
     for kb in range(Kb):
         sl = slice(kb * blockB, (kb + 1) * blockB)