Add comprehensive tests to test the kernel across available dtypes.

Added softmax and gemm kernel to test across the available float and int dtypes.

Author: Prashant Kumar

core/tests/kernel/coverage_test.py

@@ -0,0 +1,106 @@
+import torch
+import shark_turbine.kernel as tk
+import shark_turbine.kernel.lang as tkl
+import pytest
+
+
+TKL_TO_TORCH_DTYPE = {
+    tkl.f16: torch.half,
+    tkl.f32: torch.float,
+    tkl.f64: torch.double,
+    tkl.bool: torch.bool,
+    tkl.i8: torch.int8,
+    tkl.i16: torch.int16,
+    tkl.i32: torch.int32,
+    tkl.i64: torch.int64,
+}
+
+FLOAT_DTYPES = [tkl.f16, tkl.f32, tkl.f64]
+INT_DTYPES = [
+    tkl.bool,
+    tkl.i4,
+    tkl.i8,
+    tkl.i16,
+    tkl.i32,
+    tkl.i64,
+    tkl.index,
+]
+
+
+def softmax_krnl(dtype, input, output):
+    M = tkl.sym.M
+    K = tkl.sym.K
+
+    @tk.gen.thread(M)
+    def softmax_kernel(
+        input: tk.lang.InputBuffer[M, K, dtype],
+        output: tk.lang.OutputBuffer[M, K, dtype],
+    ):
+        row_index = tk.lang.program_id(0)
+        input_row = input[row_index, :]
+        numerator = tkl.exp2(input_row - tkl.max(input_row))
+        if dtype in INT_DTYPES:
+            output_row = numerator // tkl.sum(numerator)
+        else:
+            output_row = numerator / tkl.sum(numerator)
+        output[row_index, :] = output_row
+
+    with tk.gen.TestLaunchContext():
+        softmax_kernel(input, output)
+
+
+def gemm_fx_kernel(dtype, A, B, output):
+    N = tkl.sym.N
+    M = tkl.sym.M
+    K = tkl.sym.K
+    BLOCK_SIZE = tkl.sym.BLOCK_SIZE
+
+    @tk.gen.thread(N // BLOCK_SIZE, M // BLOCK_SIZE)
+    def gemm_kernel(
+        A: tkl.InputBuffer[N, K, dtype],
+        B: tkl.InputBuffer[K, M, dtype],
+        output: tkl.OutputBuffer[N, M, dtype],
+    ):
+        grid_n = tkl.program_id(0)
+        grid_m = tkl.program_id(1)
+
+        acc = None
+        # TODO: Only considering the float and integer cases.
+        if dtype in INT_DTYPES:
+            acc = tkl.constant((BLOCK_SIZE, BLOCK_SIZE), dtype, 0)
+        else:
+            acc = tkl.constant((BLOCK_SIZE, BLOCK_SIZE), dtype, 0.0)
+
+        @tkl.for_loop(0, K // BLOCK_SIZE, init_args=[acc])
+        def body(i, c):
+            a = tkl.load(A, (grid_n, i * BLOCK_SIZE), (BLOCK_SIZE, BLOCK_SIZE))
+            b = tkl.load(B, (i * BLOCK_SIZE, grid_m), (BLOCK_SIZE, BLOCK_SIZE))
+            return (tkl.dot(a, b, c),)
+
+        tkl.store(output, (grid_n, grid_m), body[0])
+
+    with tk.gen.TestLaunchContext({BLOCK_SIZE: 32}):
+        gemm_kernel(A, B, output)
+
+
+@pytest.mark.parametrize(
+    ("dtype",),
+    [(x,) for x in FLOAT_DTYPES],
+)
+def test_softmax_krnl(dtype):
+    if dtype in TKL_TO_TORCH_DTYPE:
+        input = torch.randn(128, 64).to(TKL_TO_TORCH_DTYPE[dtype])
+        output = torch.randn(128, 64).to(TKL_TO_TORCH_DTYPE[dtype])
+        softmax_krnl(dtype, input, output)
+
+
+@pytest.mark.parametrize(
+    ("dtype",),
+    [(x,) for x in FLOAT_DTYPES + INT_DTYPES],
+)
+def test_gemm_krnl(dtype):
+    if dtype in TKL_TO_TORCH_DTYPE:
+        A = torch.randn(512, 1024).to(TKL_TO_TORCH_DTYPE[dtype])
+        B = torch.randn(1024, 2048).to(TKL_TO_TORCH_DTYPE[dtype])
+        output = torch.zeros(512, 2048).to(TKL_TO_TORCH_DTYPE[dtype])
+        gemm_fx_kernel(dtype, A, B, output)