implement group gemm for contiguous case

config.py

@@ -63,12 +63,19 @@
             'h100': 'kernels/torch_scaled/scaled_matmul.cu'
         }
     },
+    'group_gemm': {
+        'source_files': {
+            'h100': 'kernels/group_gemm/group_gemm.cu'
+        }
+    },
+
 }
 
 ### WHICH KERNELS DO WE WANT TO BUILD?
 # (oftentimes during development work you don't need to redefine them all.)
 # kernels = ['attn', 'mamba2', 'hedgehog', 'fftconv', 'fused_rotary', 'based', 'fused_layernorm']
-kernels = ['fp8_gemm']
+# kernels = ['fp8_gemm']
+kernels = ['group_gemm']
 
 ### WHICH GPU TARGET DO WE WANT TO BUILD FOR?
 target = 'h100'

kernels/group_gemm/gemm_test.py

@@ -0,0 +1,173 @@
+import torch
+
+import random
+import torch
+from typing import List, Tuple
+
+import thunderkittens
+import deep_gemm
+from deep_gemm import bench_kineto, calc_diff, ceil_div, get_col_major_tma_aligned_tensor
+from deep_gemm.jit_kernels.utils import get_m_alignment_for_contiguous_layout
+
+torch.manual_seed(42)
+
+
+def calc_diff(x, y):
+    x, y = x.double(), y.double()
+    denominator = (x * x + y * y).sum()
+    sim = 2 * (x * y).sum() / denominator
+    return 1 - sim
+
+def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    pad_size = (128 - (n % 128)) % 128
+    x = torch.nn.functional.pad(x, (0, pad_size), value=0) if pad_size > 0 else x
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    fp8_data = (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn)
+    return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
+
+
+def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros((ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device)
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
+
+
+def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int, bf16_input=False) -> \
+        Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
+    alignment = get_m_alignment_for_contiguous_layout()
+    # group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3)) for _ in range(num_groups)]
+    group_ms = [expected_m_per_group  for _ in range(num_groups)]
+    m = sum([ceil_div(x, alignment) * alignment for x in group_ms])
+
+    x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
+    y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)    
+    m_indices = torch.empty(m, device='cuda', dtype=torch.int32)
+    out = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
+    ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)
+
+    start = 0
+    for i, group_m in enumerate(group_ms):
+        actual_end = start + group_m
+        aligned_end = start + ceil_div(group_m, alignment) * alignment
+        m_indices[start:actual_end] = i
+        m_indices[actual_end:aligned_end] = i
+        ref_out[start:aligned_end] = x[start:aligned_end] @ y[i].t()
+        start = aligned_end
+    ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
+
+    assert m % 4 == 0, f'TMA alignment error: {m}'
+    if bf16_input:
+        return m, x, y, m_indices, out, ref_out
+    x_fp8 = per_token_cast_to_fp8(x)
+    y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), k // 128), device='cuda', dtype=torch.float))
+    for i in range(num_groups):
+        y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
+
+    return m, x_fp8, y_fp8, m_indices, out, ref_out
+
+
+def test_m_grouped_gemm_contiguous_deepgemm() -> None:
+    print('Testing grouped contiguous GEMM for deepgemm:')
+
+    for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168),
+                                                   (8, 4096, 7168, 4096), (8, 4096, 2048, 7168),
+                                                   (32, 256, 7168, 4096), (32, 256, 2048, 7168)):
+        # NOTES: we should mask the unfilled part before calculating difference
+        m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
+        deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
+        out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out)
+        diff = calc_diff(out, ref_out)
+        assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
+
+        # noinspection PyShadowingNames
+        def test_func():
+            deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
+
+        t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
+        valid_m = (m_indices != -1).sum().item()
+        print(f' > Perf ({num_groups=:2}, {expected_m_per_group=:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
+              f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
+              f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
+    print()
+
+
+def test_m_grouped_gemm_masked_deepgemm() -> None:
+    print('Testing grouped masked GEMM:')
+
+    for num_groups, expected_m_per_group in ((1, 1024), (2, 512), (4, 256)):
+        for k, n in ((7168, 4096), (2048, 7168), ):
+            # Test correctness
+            for i in range(10):
+                x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, 4096, expected_m_per_group, k, n)
+                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
+                for j in range(num_groups):
+                    diff = calc_diff(out[j, :masked_m[j].item()], ref_out[j, :masked_m[j].item()])
+                    assert diff < 0.001, f'{expected_m_per_group=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
+
+            # noinspection PyShadowingNames
+            def test_func():
+                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
+
+            # Test performance with fixed shapes
+            valid_m = masked_m.sum().item()
+            t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
+            print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
+                  f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
+                  f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
+    print()
+
+def test_m_grouped_gemm_contiguous_tk() -> None:
+    print('Testing grouped contiguous GEMM for tk:')
+
+    for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168),
+                                                   (8, 4096, 7168, 4096), (8, 4096, 2048, 7168),
+                                                   (32, 256, 7168, 4096), (32, 256, 2048, 7168)):
+        # NOTES: we should mask the unfilled part before calculating difference
+        m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
+        deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
+        out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out)
+
+        ht_out = torch.empty_like(out)
+        thunderkittens.group_gemm(
+            x_fp8[0], # x_fp8 is a tuple (x, x_inv_s)
+            y_fp8[0], # y_fp8 is a tuple (y, y_inv_s)
+            x_fp8[1].transpose(0, 1).contiguous(), # x_inv_s
+            y_fp8[1],
+            m_indices,
+            ht_out)
+        ht_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), ht_out)
+        diff = calc_diff(ht_out, ref_out)
+        assert diff < 0.001, f'HT has big difference with float: {m=}, {k=}, {n=}, {diff:.5f}'
+        assert torch.allclose(out, ht_out.bfloat16(), atol=1e-3, rtol=1e-2), f'HT has big difference with deepgemm: {m=}, {k=}, {n=}'
+
+        # tk scale shape need to b (k, m)
+        x_fp8 = (x_fp8[0], x_fp8[1].transpose(0, 1).contiguous())
+        def test_func():
+            thunderkittens.group_gemm(
+                x_fp8[0], # x_fp8 is a tuple (x, x_inv_s)
+                y_fp8[0], # y_fp8 is a tuple (y, y_inv_s)
+                x_fp8[1], # x_inv_s
+                y_fp8[1],
+                m_indices,
+                ht_out)
+
+        t = bench_kineto(test_func, 'matmul_template', suppress_kineto_output=True)
+        valid_m = (m_indices != -1).sum().item()
+        print(f' > Perf ({num_groups=:2}, {expected_m_per_group=:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
+              f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
+              f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
+
+    print()
+
+
+if __name__ == '__main__':
+    test_m_grouped_gemm_contiguous_deepgemm()
+    test_m_grouped_gemm_contiguous_tk()