Introduce FMA lowering for DotOp.

python/tutorials/03-matrix-multiplication-cpu.py

@@ -153,14 +153,35 @@
 
 import triton
 import triton.language as tl
+import os
 
-BLOCK_SIZE_M = 32
+DTYPE = getattr(torch, (os.getenv("DTYPE", "float32")))
+# Chosse block size depending on dtype. We have more register
+# capacity for bfloat16/float16 compared to float32.
+BLOCK_SIZE_M = 8 if DTYPE == torch.float32 else 32
 BLOCK_SIZE_N = 32
-BLOCK_SIZE_K = 32
+BLOCK_SIZE_K = 8 if DTYPE == torch.float32 else 32
+CACHE_PADDING = os.getenv("CACHE_PADDING", "0") != "0"
+PREPACKED = os.getenv("PREPACKED", "0") != "0"
+PAD_B_ONLY = True
 GROUP_SIZE_M = 8
 USE_GPU = False
 
 
+@triton.jit
+def pad_kernel(in_ptr, out_ptr, N, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, PADDING: tl.constexpr):
+    in_offset = tl.program_id(axis=0) * N * BLOCK_SIZE_M
+    out_offset = tl.program_id(axis=0) * (N + PADDING) * BLOCK_SIZE_M
+    for row in tl.range(0, BLOCK_SIZE_M):
+        for block in tl.range(0, N // BLOCK_SIZE_N):
+            val = tl.load(in_ptr + in_offset + block * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N))
+            tl.store(out_ptr + out_offset + block * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N), val)
+        zero = tl.full((PADDING, ), 0, dtype=in_ptr.type.element_ty)
+        tl.store(out_ptr + out_offset + N + tl.arange(0, PADDING), zero)
+        in_offset += N
+        out_offset += N + PADDING
+
+
 @triton.jit
 def matmul_kernel(
         # Pointers to matrices
@@ -198,14 +219,12 @@ def matmul_kernel(
     # Create pointers for the first blocks of A and B.
     # We will advance this pointer as we move in the K direction
     # and accumulate
-    # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
-    # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
-    # See above `Pointer Arithmetic` section for details
-    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
-    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
-    offs_k = tl.arange(0, BLOCK_SIZE_K)
-    a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
-    b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
+    block_offset_m = pid_m * BLOCK_SIZE_M
+    block_offset_n = pid_n * BLOCK_SIZE_N
+    a_tile_ptr = tl.make_block_ptr(base=a_ptr, shape=(M, K), strides=(stride_am, stride_ak),
+                                   offsets=(block_offset_m, 0), block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_K), order=(1, 0))
+    b_tile_ptr = tl.make_block_ptr(base=b_ptr, shape=(K, N), strides=(stride_bk, stride_bn),
+                                   offsets=(0, block_offset_n), block_shape=(BLOCK_SIZE_K, BLOCK_SIZE_N), order=(1, 0))
 
     # -----------------------------------------------------------
     # Iterate to compute a block of the C matrix.
@@ -217,43 +236,50 @@ def matmul_kernel(
         # Load the next block of A and B, generate a mask by checking the K dimension.
         # If it is out of bounds, set it to 0.
 
-        # TODO: Currently masked load is not supported yet.
-        # a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
-        # b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
-        a = tl.load(a_ptrs)
-        b = tl.load(b_ptrs)
+        a = tl.load(a_tile_ptr)
+        b = tl.load(b_tile_ptr)
         # We accumulate along the K dimension.
         accumulator = tl.dot(a, b, accumulator, out_dtype=tl.float32)
         # Advance the ptrs to the next K block.
-        a_ptrs += BLOCK_SIZE_K * stride_ak
-        b_ptrs += BLOCK_SIZE_K * stride_bk
+        a_tile_ptr = tl.advance(a_tile_ptr, [0, BLOCK_SIZE_K])
+        b_tile_ptr = tl.advance(b_tile_ptr, [BLOCK_SIZE_K, 0])
 
     # Convert the accumulator to the output matrix C's type if needed.
     c = accumulator
 
     # -----------------------------------------------------------
-    # Write back the block of the output matrix C with masks.
-    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
-    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
-
-    # TODO: Currently masked load is not supported yet.
-    # c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
-    # tl.store(c_ptrs, c, mask=c_mask)
-    tl.store(c_ptrs, c)
+    # Write back the block of the output matrix C.
+    c_block_ptr = tl.make_block_ptr(base=c_ptr, shape=(M, N), strides=(stride_cm, stride_cn),
+                                    offsets=(block_offset_m, block_offset_n), block_shape=(BLOCK_SIZE_M, BLOCK_SIZE_N),
+                                    order=(1, 0))
+    tl.store(c_block_ptr, c)
 
 
 # %%
 # We can now create a convenience wrapper function that only takes two input tensors,
 # and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel.
 
+a_scratch = torch.empty((), dtype=DTYPE)
+b_scratch = torch.empty((), dtype=DTYPE)
 
-def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
+
+def matmul_preprocess_input(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
     # Check constraints.
     assert a.shape[1] == b.shape[0], "Incompatible dimensions"
     assert a.is_contiguous(), "Matrix A must be contiguous"
     M, K = a.shape
     K, N = b.shape
+
+    # TODO: Check if padding is needed at all.
+    if CACHE_PADDING:
+        a_scratch.resize_(M, K + 32)
+        b_scratch.resize_(K, N + 32)
+        if not PAD_B_ONLY:
+            pad_kernel[(M // BLOCK_SIZE_M, )](a, a_scratch, K, BLOCK_SIZE_M, BLOCK_SIZE_K, 32, num_threads=num_threads)
+            a = a_scratch
+        pad_kernel[(K // BLOCK_SIZE_K, )](b, b_scratch, N, BLOCK_SIZE_K, BLOCK_SIZE_N, 32, num_threads=num_threads)
+        b = b_scratch
+
     #TODO: Currently masked load is not supported yet.
     assert (M % BLOCK_SIZE_M == 0) and (N % BLOCK_SIZE_N == 0) and (
         K % BLOCK_SIZE_K == 0), "Masking currently not supported, Matrix dimensions must be multiples of block size"
@@ -262,6 +288,14 @@ def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
         c = torch.empty((M, N), device=a.device, dtype=a.dtype)
     else:
         assert c.shape == (M, N), "Incompatible dimensions"
+
+    return a, b, c
+
+
+def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, M: int, N: int, K: int, num_threads=0):
+    if not PREPACKED:
+        a, b, c = matmul_preprocess_input(a, b, c, num_threads=num_threads)
+
     # 1D launch kernel where each block gets its own program.
     grid = (triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N), )
     matmul_kernel[grid](
@@ -287,10 +321,13 @@ def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
 
 triton.runtime.driver.set_active_to_cpu()
 
-a = torch.randn((512, 512), device='cpu', dtype=torch.float32)
-b = torch.randn((512, 512), device='cpu', dtype=torch.float32)
-triton_output = matmul(a, b, None)
-torch_output = torch.matmul(a, b)
+a = torch.randn((512, 512), device='cpu', dtype=DTYPE)
+b = torch.randn((512, 512), device='cpu', dtype=DTYPE)
+c = None
+torch_output = torch.matmul(a.to(torch.float32), b.to(torch.float32))
+if PREPACKED:
+    a, b, c = matmul_preprocess_input(a, b, c)
+triton_output = matmul(a, b, c, 512, 512, 512)
 print(f"triton_cpu_output_with_{a.dtype}_inputs={triton_output}")
 print(f"torch_cpu_output_with_{a.dtype}_inputs={torch_output}")
 rtol = 0
@@ -310,9 +347,9 @@ def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
 # We can now compare the performance of our kernel against that of Pytorch. Here we focus on square matrices,
 # but feel free to arrange this script as you wish to benchmark any other matrix shape.
 
-LINE_VALS = ['triton-cpu-single', 'triton-cpu', 'torch-cpu-native', 'torch-cpu-compile']
-LINE_NAMES = ['TritonCPU 1', 'TritonCPU', 'TorchCPU (native)', 'TorchCPU (compile)']
-LINE_STYLES = [('blue', '--'), ('blue', '-'), ('green', '--'), ('green', '-')]
+LINE_VALS = ['triton-cpu-single', 'triton-cpu', 'torch-cpu-native']
+LINE_NAMES = ['TritonCPU 1', 'TritonCPU', 'TorchCPU (native)']
+LINE_STYLES = [('blue', '--'), ('blue', '-'), ('green', '--')]
 
 if USE_GPU and triton.runtime.driver.get_active_gpus():
     triton.runtime.driver.set_active_to_gpu()
@@ -356,36 +393,47 @@ def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, num_threads=0):
         ylabel='GFLOPS',  # Label name for the y-axis.
         plot_name=
         # Name for the plot. Used also as a file name for saving the plot.
-        f'matmul-performance-fp32 (BLOCK_SIZE_M={BLOCK_SIZE_M}, BLOCK_SIZE_N={BLOCK_SIZE_N}, BLOCK_SIZE_K={BLOCK_SIZE_K}, GROUP_SIZE_M={GROUP_SIZE_M})',
+        f'matmul-performance-{DTYPE} (CACHE_PADDING={CACHE_PADDING} PREPACKED={PREPACKED} PAD_B_ONLY={PAD_B_ONLY} GROUP_SIZE_M={GROUP_SIZE_M})',
         args={},  # Values for function arguments not in `x_names` and `y_name`.
     ))
 def benchmark(M, N, K, provider):
 
     device = 'cpu' if 'cpu' in provider else 'cuda'
-    a = torch.randn((M, K), device=device, dtype=torch.float32)
-    b = torch.randn((K, N), device=device, dtype=torch.float32)
+    a = torch.randn((M, K), device=device, dtype=DTYPE)
+    b = torch.randn((K, N), device=device, dtype=DTYPE)
 
     if device == 'cpu':
-        c = torch.empty((M, N), device=a.device, dtype=a.dtype)
+        if 'triton-cpu' in provider:
+            c = torch.zeros((M, N), device=a.device, dtype=torch.float32)
+        else:
+            c = torch.zeros((M, N), device=a.device, dtype=a.dtype)
         triton.runtime.driver.set_active_to_cpu()
     else:
         c = None
         triton.runtime.driver.set_active_to_gpu()
 
+    if PREPACKED:
+        triton_a, triton_b, triton_c = matmul_preprocess_input(a, b, c)
+    else:
+        triton_a, triton_b, triton_c = a, b, c
+
     quantiles = [0.5, 0.2, 0.8]
     if provider == 'torch-gpu':
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), quantiles=quantiles)
     elif provider == 'triton-gpu':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b, None), quantiles=quantiles)
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(triton_a, triton_b, None), quantiles=quantiles)
     elif provider == 'torch-cpu-native':
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b, out=c), quantiles=quantiles)
     elif provider == 'torch-cpu-compile':
         compiled = torch.compile(torch.matmul)
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: compiled(a, b, out=c), quantiles=quantiles)
     elif provider == 'triton-cpu-single':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b, c, num_threads=1), quantiles=quantiles)
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: matmul(triton_a, triton_b, triton_c, M, N, K, num_threads=1), quantiles=quantiles,
+            measure_time_with_hooks=True)
     elif provider == 'triton-cpu':
-        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b, c), quantiles=quantiles)
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(triton_a, triton_b, triton_c, M, N, K),
+                                                     quantiles=quantiles, measure_time_with_hooks=True)
     perf = lambda ms: 2 * M * N * K * 1e-9 / (ms * 1e-3)
     return perf(ms), perf(max_ms), perf(min_ms)